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316

BONIPHACE ELPHACE KANYATHARE

DEVELOPMENT OF OPTICAL MEASUREMENT TECHNIQUES AND DATA ANALYSIS METHODS FOR SCREENING

OF ADULTERATED DIESEL OILS


Fuel adulteration is one of the contributors to climate change. As a partial solution to

fuel adulteration, this thesis proposes novel approaches by using handheld refractometer

and novel handheld sensor to combat fuel adulteration especially in field conditions.

Moreover, for the first time in applied optics the concepts of excess permittivity and

imaginary excess permittivity are applied to resolve complex fuel adulteration problems.

These novel approaches save as new openings demonstrating the potential of excess

permittivity analysis in fraud fuel detection.


30887153_UEF_Vaitoskirja_NO_316_Elphace_Boniphace_LUMET_cover_18_09_12.indd 1 12.9.2018 8.51.23

DEVELOPMENT OF OPTICAL

MEASUREMENT TECHNIQUES AND

DATA ANALYSIS METHODS FOR

SCREENING OF ADULTERATED

DIESEL OILS


DEVELOPMENT OF OPTICAL

MEASUREMENT TECHNIQUES AND

DATA ANALYSIS METHODS FOR

SCREENING OF ADULTERATED

DIESEL OILS

Publications of the University of Eastern Finland


No 316

University of Eastern Finland

Joensuu

2018

Academic dissertation

To be presented by permission of the Faculty of Science and Forestry

for public examination in the Auditorium M100 in the Metria Building

at the University of Eastern Finland, Joensuu, on October 15, 2018, at 12

o’clock noon

Grano Oy

Jyväskylä, 2018

Editors: Pertti Pasanen, Matti Vornanen,

Jukka Tuomela, Matti Tedre

Distribution: University of Eastern Finland / Sales of publications

www.uef.fi/kirjasto

ISBN: 978-952-61-2893-1 (nid.)

ISBN: 978-952-61-2894-8 (PDF)

ISSNL: 1798-5668

ISSN: 1798-5668

ISSN: 1798-5676 (PDF)

Author’s address: University of Eastern Finland

Depart. of Physics and Mathematics

P.O. Box 111

80101 JOENSUU, FINLAND

email: [email protected]

Supervisors: Professor Kai-Erik Peiponen



P.O. Box 111



Professor Seppo Honkanen



P.O. Box 111



Reviewers: Associate Professor Juha Toivonen

Tampere University of Technology

Laboratory of Photonics

P.O.BOX 692

33101 TAMPERE, FINLAND


Adjunct Professor Ilpo Niskanen

University of Oulu

Thule Institute Research Centre

P.O. Box 8000

OULU, FINLAND


Opponent: Professor Valerio Lucarini

University of Reading

Department of Mathematics & Statistics

Whiteknights, P.O.BOX 220

READING RG6 6AX, UK


mailto:[email protected]





i

Kanyathare, Boniphace Elphace

Development of Optical Measurement Techniques and Data analysis Methods for

screening of adulterated Diesel oils.

Joensuu: University of Eastern Finland, 2018

Publications of the University of Eastern Finland

Dissertations in Forestry and Natural Sciences 2018; 316

ISBN: 978-952-61-2893-1 (print)

ISSNL: 1798-5668

ISSN: 1798-5668

ISBN: 978-952-61-2894-8 (PDF)

ISSN: 1798-5676 (PDF)

ABSTRACT

Climate change is one of the major challenges and threats facing our planet, with one

of its main contributors being environmental pollution, which is caused in part by

fuel adulteration. Because of cheap price of kerosene in developing countries as com-

pared to other fuels, adulteration of diesel oils by kerosene is highly prevalent in

those countries, which is also among the difficult cases to screen by conventional

techniques in particular when the volume of kerosene is below 20%. As a remedy to

this problem, we have developed and demonstrated several optical measurement

techniques and data analysis methods for screening very low levels of kerosene adul-

teration in diesel oils, namely 5%, 10% and 15%.

This we have achieved firstly, by developing an optical sensor which is a modifi-

cation of commercial handheld gloss meter, by incorporating a removable sensor

head with roughened glass for screening of the afore mentioned low levels of adul-

teration. Secondly, we have developed two data analysis methods that make use of

the refractive index measurements as well as transmittance data inversion using sin-

gly subtractive Kramers-Kronig (SSKK) relations. The results of SSKK were applied

to determine wavelength-dependent relative excess permittivity and wavelength-de-

pendent imaginary excess permittivity, which not only reveal the hidden spectral

finger prints, but also discriminate different adulteration levels. Thirdly, we have

also demonstrated the application of cheap commercial handheld refractometer (tra-

ditionally used for glucose concentration measurements), for fuel adulteration detec-

tion as an alternative especially for poor countries.

This thesis demonstrates two novel ideas, namely it reports on excess permittivity

study of diesel oils adulterated by kerosene, and on the application of general Kra-

mers-Kronig (K-K) analysis which is based on the principle of causality, for assessing

both real excess permittivity and imaginary excess permittivity of binary liquid mix-

tures, especially for the case of adulterated fuels.

ii

We believe that the concepts demonstrated in this thesis will open more doors for

other possible applications far beyond liquid fuels to other areas such as olive oil,

palm oil, and even liquid food adulteration. Moreover, we do believe that the results

of this work will aid into the development of more affordable and portable field-

based sensors for liquid fuel purity measurements.

Universal Decimal Classification: 535.324, 665.7.035.7, 665.753, 681.785.2

Library of Congress Subject Headings: Liquids—Optical properties; Liquid fuels; Diesel

fuels; Adulterations; Kerosene; Optics; Optical measurements; Optical detectors; Refractive

index; Refractometers; Quality control

Yleinen suomalainen asiasanasto: nesteet; moottoripolttoaineet; dieselöljy;

väärentäminen; petroli; optiikka; optiset ominaisuudet; optiset anturit; laadunvalvonta

iii

ACKNOWLEDGEMENTS

I have grown old enough to understand and admit the undeniable fact that we as

individuals cannot survive and get ahead in life as island, without the support and

cooperation of people around us. In fact, the realization of this thesis is a testimony

to this fact. Regardless of our nationalities, ethnicities or race, we can all cooperate

and work together to make our world a better place for future generations.

From the bottom of my heart I would like to offer my sincere and deep grounded

gratitude to my supervisor Prof. K. -E. Peiponen for enabling me to come this far in

such short duration. Dear Kai, to tell it plainly you have played more than a fatherly

role, your guidance, coaching, motivation, correction, encouragement, dedication

and patience, has played a key and vital role without which I wouldn’t have come

this far. All I can say is, God bless you. To my second supervisor and past head of

Physics and Mathematics department Prof. S. Honkanen, thank you for your inspi-

ration and for accepting to becoming part of this awesome journey.

To co-authors Dr. J. Räty and Dr. K. Kuivalainen, thank you for the samples and

for the measurements which contributed to this work. To co-authors Dr. P. Bawuah

and P. Silfsten, thank you for your overwhelming support and cooperation from the

earliest stages of this work. To Dr. M. Markinnen and Dr. M. Silvennoinen thank you

for the countless hours you spent with me in the lab taking measurements. To Msc.

B. Asamoah, thank you for time dependent measurements and for your collabora-

tion.

I wish to express my sincere gratitude and appreciation to other members of the

department of physics and mathematics UEF, Noora Heikkilä (coordinator), Prof. P.

Vahimaa (Director of the institute of photonics), Prof. T. Jääskeläinen (Former HOD),

Prof. M. Kuittinen, Prof. M. Roussey, Prof. Y. Svirko, and lastly but not least Dr. K.

Saastamoinen. Your support, cooperation and guidance during my studies created a

special and awesome environment, that has enabled me to come this far. I say a big

thank you.

To my Wife, Mother, and children. Thank you for enriching my life and for put-

ting smile on my face constantly.

This thesis is dedicated to Aino Peiponen who passed away during the final stages

of this thesis, her memory shall remain in our hearts because it is through her that

Kai come into this world.

Finally, I acknowledge the sovereign God for free breath of life, that he has

granted us. Moreover, I am greatful for the lessons that he has taught me through

challenging as well as wonderful life experiences. These experiences have forever

changed my life for the better.

Joensuu, 15th of October 2018

Boniphace Elphace Kanyathare

iv

LIST OF ABBREVIATIONS

GC-MS Gas chromatography-mass spectrometry

IC-MS Inductively coupled mass spectrometry

HPLC High performance liquid chromatography

CD Compact disk

DVD Digital video disk

NIR Near infrared

PCA Principal component analysis

LDA Linear discriminant nalysis

PCR Principal component regression

PLS Partial least square

SIMCA Soft independent modelling of class analogy

ASTM American Society of Testing and Materials international

GC Gas chromatography

EN European Norms

Vis Visible light

SSKK Singly subtractive Kramers-Kronig analysis

KK Kramers-Kronig analysis

LPG Liquified petroleum gas

ATF Aviation turbine fuel

PM Particulate matter

PAHS Polycyclic aromatic hydrocarbons

VOC Volatile organic compounds

MIR Mid Infrared

FTIR Fourier Transform Infrared

SFS Synchronous fluorescence scan

EEMF Excitation emission matrices fluorescence

ISO International organization for standardization

PACS Polycyclic aromatic compounds

MTBE Methyl tetra butyl ether

EWURA Energy and water utilities regulatory authority

MSC Multiplicative scatter correction

UV Ultraviolet light

AC Alternating current

DOE Diffractive optical element

CGH Computer generated hologram

v

LIST OF ORIGINAL PUBLICATIONS This thesis is based on data presented in the following articles, referred to by the

Roman Numerals I-IV.

I. Kanyathare B., Kuivalainen K., Räty J., Silfsten P., Bawuah P. &

Peiponen K.-E. 2018. A prototype of an optical sensor for the identifica-

tion of diesel oil adulterated by kerosene. Journal of European Optical So-

ciety Rapid Publications 14: 1-6.

II. Kanyathare B. & Peiponen K.-E. 2018. Wavelength-dependent excess

permittivity as indicator of kerosene in diesel oil. Applied Optics 57: 2997-

3002.

III. Kanyathare B., Asamoah B. & Peiponen K.-E. 2018. Imaginary excess

permittivity in NIR-spectral range for separation and discrimination of

adulterated diesel oil binary mixtures (submitted).

IV. Kanyathare B. & Peiponen K.-E. 2018. Handheld Refractometer based

measurement and excess permittivity analysis method for detection of

diesel oils adulterated by kerosene in field conditions. Sensors 18, 1551.

vi

AUTRHOR’S CONTRIBUTION The ideas for all the papers in this thesis were developed based on intensive discus-

sions between the author and his Supervisor. The first paper was a result of nice

group effort where the author played a key role in all the measurements as well as

manuscript writing. In the subsequent three works, the author did most part of the

measurements including all computations and wrote the articles in collaboration

with and guidance of the supervisor. The author dealt with all submissions and all

the collaborations during the entire review and publication process, as the main

and corresponding author.

vii

TABLE OF CONTENTS 1 INTRODUCTION ................................................................................. 1

2 LIQUID FUEL ADULTERATION ......................................................... 5 2.1 Diesel oil ...................................................................................................... 5 2.2 Kerosene ..................................................................................................... 6 2.3 Adulteration .................................................................................................. 6

3 THEORY ............................................................................................ 10 3.1 Light interaction with the roughened glass-fuel interface ..........................11

3.1.1 Description adopted from the pigment model .....................................11 3.2 The wetting property of liquids (Contact angle) .........................................13

3.2.1 Ideal wetting process ..........................................................................13 3.2.2 Contact angle hysteresis .....................................................................14 3.2.3 Behavior of liquid over a rough surface ..............................................15

3.3 Beer-Lambert’s law ....................................................................................16 3.4 Complex refractive index ...........................................................................17 3.5 Singly subtractive Kramers-Kronig relation (SSKK) ..................................17 3.6 Complex excess permittivity ......................................................................18 3.7 Lorentz-Lorenz formula..............................................................................19 3.8 Modified ideal law of binary mixtures .........................................................20

4 OPTICAL MEASUREMENTS ............................................................ 21 4.1 Optical signal measurement (Prototype) ...................................................21 4.2 Description of the training set ....................................................................22 4.3 Refractive index measurements in Finland ...............................................23 4.4 Refractive index measurements in Tanzania ............................................25 4.5 Transmittance measurements ...................................................................26

4.5.1 Double optical path length method (Authentic samples) ....................26 4.5.2 Adulterated samples ...........................................................................28

5 DATA ANALYSIS METHODS ........................................................... 30 5.1 Extinction coefficient ..................................................................................30 5.2 Real refractive index by singly subtractive Kramers-Kronig relation .........30 5.3 Excess permittivity .....................................................................................31 5.4 Imaginary excess permittivity ....................................................................31 5.5 Volume increase method (Handheld Abbe) ..............................................31

6 RESULTS AND DISCUSSION........................................................... 33 6.1 Prototype of an optical sensor ...................................................................33

6.1.1 Detected optical signal ........................................................................33 6.1.2 Contact angle measurements .............................................................35 6.1.3 Dynamic signal from fuel drop spreading over a rough glass .............35 6.1.4 Summary .............................................................................................37

6.2 Extinction coefficient ..................................................................................37

viii

6.3 Wavelength dependent refractive index ....................................................39 6.4 Excess optical properties ...........................................................................42

6.4.1 Excess permittivity ..............................................................................42 6.4.2 Imaginary excess permittivity ..............................................................43 6.4.3 Summary .............................................................................................45

6.5 Handheld refractometer method ................................................................46 6.5.1 Training set .........................................................................................46 6.5.2 Field measurements ............................................................................47 6.5.3 Summary .............................................................................................48

7 CONCLUSION AND OUTLOOK ........................................................ 49

8 BIBLIOGRAPHY ............................................................................... 50

1

1 INTRODUCTION

The problem of fuel adulteration continues to negatively impact our planet and it is

present at alarming levels not only in developing countries and third world coun-

tries, but also in some parts of Europe (Bhanu P., 2017; Kalligeros S., 2003). Petroleum

products are the backbone of any economy in the world because of high demand,

among these, kerosene is usually subsidized especially for poor and developing

countries. For these countries, kerosene is therefore a lucrative product for adulter-

ation, because it has similar chemical properties with diesel oil and gasoline (Majhi

A., 2012; Bhanu P., 2017).

Liquid fuel adulteration results into environmental pollution which is the big

challenge facing the globe currently. It is one of the major contributors to atmospheric

ozone pollution (Marais E. A., 2014), and soot carbon (Schuster G. L., 2016). Moreo-

ver, adulteration contributes to black carbon, which further increases earths average

temperature inducing more far reaching negative impacts (Majhi A., 2012; Speight J.

G, 2015; Gupta A. & Sharma R, 2010; Sinha S. N. & Shivgotra V, 2012; Sadat A, 2014).

Liquid fuel adulteration is not the only type of adulteration affecting the world

community, the other more severe category of liquid adulteration has direct impact

on human health because it deals with liquid foods as well as oils that are consumed

by humans. These includes, olive oil adulteration (Isabel D. M., 2018), alcohol prod-

ucts such as wines and whiskey (Lachenmeier D. W. & Rehm J, 2016; Lachenmeier

D. W. & Rehm J, 2013; Lachenmeier D. W., 2007). Moreover, beverage adulteration is

also a big issue (Maireva S., 2013; Ogrinc N., 2003), and milk adulteration (Tanzina

A. & Shoeb A, 2016; Moore J. C., 2012). Milk adulteration is even more severe, because

it affects the lives of infants. Even though, the mentioned types of adulteration are

not dealt with in this work, we do believe that the findings of this work can aid in

resolving some of those.

Traditionally liquid measurements and identification is based on density utilizing

hydrometers (Gupta S, 2002; Guay R. R., 1983; Demin V, 2007). However, simple den-

sity of liquids measurement can be misleading in the case of sensitive measurement

requirements like adulterated diesel oil by kerosene, because of very close density

values between the two constituent liquids. Other than density measurements, other

traditional measurements include gas chromatography-mass spectrometry (GC-MS),

inductively coupled-mass spectrometry (IC-MS), and high performance-liquid chro-

matography (HPLC) (Bhanu P., 2017), these methods are invasive, laborious and time

consuming. To overcome these challenges and enable timely and accurate measure-

ment of liquids, optical based measurement methods such as refractive index and

optical spectroscopy are potential candidates.

The refractive index of a material whether solid or liquid is not only directly re-

lated to the density of that material, but also related to temperature and pressure.

This implies that, the purity of material as well as other external factors affects the

2

refractive index. Therefore, when the adulterants are added into pure fuel samples,

purity of the samples is altered, leading to change in refractive index (Räty J. & Peipo-

nen K. -E, 2015). Refractive index readings can be utilized to characterize materials,

either on their own or can further be applied in advanced data analysis methods for

purity identification. This quantity has been utilized for purity measurement of liq-

uids in different studies (Payri R., 2013; Geacai S., 2012; Polynkin P., 2005; Kim C. -B.

& Su C. B, 2004; Magnusson R., 2010; Fernandes V. H, 2008; Mishra V., 2008;

Ariponnammal S., 2012).

The other very important arm of optical measurement techniques is optical spec-

troscopy, thanks to the presence of organic molecules fingerprints in infrared spectral

region, the quality of liquids such as fuels can be tested (Workman J, 1996; Keifer J,

2015). This is possible because material molecules such as hydrocarbons and other

functional groups, absorb the incident electromagnetic radiation especially in the

near infrared (NIR) spectral range. Recently, the applications of optical spectroscopy

have moved further beyond the early traditional medical and industrial fields, to se-

curity both in civil and military environments (Peiponen K. -E. & Saarinen J. J, 2009).

Moreover, these techniques are also prevalent in consumer devices such as computer

mice, fingerprint readers, barcode scanners as well as CD and DVD’s (Oksman A,

2008).

The NIR spectra is achieved through spectrophotometric measurements which

scans the wavelength of the incident radiation either in transmission, reflectance or

absorbance mode. these types of measurements have been demonstrated and applied

not only in the rapid measurement of diesel engine oil quality, but also in the inves-

tigation of chain of custody for crude oil samples (SongQing Z., 2012; Soraya S. B.,

2006). Diesel oils and kerosene have overlapping spectral fingerprints in the NIR re-

gion which complicates the adulteration detection process and is probably the reason

for the failure of techniques for screening adulteration levels below 20%. Therefore,

the process of differentiating adulterated fuels only based on either transmittance or

reflectance measurement data alone is usually impossible. Not long ago, several in-

teresting measurements and data analysis methods were proposed for screening of

fake liquid fuels (Taksande A. & Hariharan C, 2006; Bassbasi M., 2013; Paiva E. M.,

2015). Moreover, the liquid purity studies detection using optical spectroscopy have

grown because of studies of biodiesel and bioethanol products (Golebiowski J. & Pro-

hun T, 2008; Kontturi V., 2011; Ventura M., 2013).

The potential of optical spectroscopy in liquid fuel studies is unquestionable. In

recent years terahertz time-domain spectroscopy has also found way in these studies.

It has been applied to quantify gasoline-diesel mixtures (Yi-nan L., 2013), in the anal-

ysis of petroleum products and their mixtures (Yun-Sik J., 2008), in discriminating

gasoline fuel contamination in engine oils (Abdul-Munaim A. M., 2018). Moreover,

it has been applied for studying the dielectric properties of diesel and gasoline (Arik

E., 2014), for quantitative distinction of gasoline mixtures (Li Y., 2013), in sensing of

petroleum industrial applications (Al-Douseri F. M., 2006), and finally in determin-

ing spectral features of commercial derivative fuel oils (Zhao H., 2012).

3

Another method based on spectroscopy that is gaining acceptance in fuel adul-

teration studies is multivariate analysis such as PCA, PCR, PLS, SIMCA, and many

others. Recently, the potential of these methods was demonstrated by several re-

searchers (Marcio J. C. P., 2011; Fazal M., 2017; Silva A.C., 2012) to uncover liquid

fuel adulteration. One of the reasons why multivariate analysis is necessary in fuel

adulteration studies is that, the traditional testing methods such as ASTM 4052, and

EN 14078 measure the absorbance at only one wavelength (Marcio J. C. P., 2011).

These methods can be compromised especially for the case of diesel and kerosene,

because they have the same chemical composition. Multivariate analysis has lots of

potential in solving different liquid measurement challenges as well as solid samples

and other materials. However, it requires many samples and knowledge about sta-

tistical methods (Swierenga H, 1995). These methods are very efficient especially un-

der laboratory conditions but requires a broad spectral range which is a drawback

especially in field conditions.

This thesis focuses on studying adulteration of diesel with kerosene, because most

of the existing methods in literature for fuel adulteration deal with a relatively simple

case in the sense of measurement, namely, gasoline adulteration using kerosene. For

this purpose, the complex excess relative permittivity was introduced. The excess

relative permittivity of liquid mixtures is highly important in assessing the interac-

tions between different molecules of different liquid components in the liquid mix-

ture (Reis J. C., 2009). Depending whether the excess permittivity value is positive,

negative or zero, it can be exploited for estimating the presence or absence of dipole-

dipole interactions between the liquid molecules (Ahire S., 1998). Theoretical foun-

dations on the complex excess permittivity that depends on the frequency of the in-

cident electromagnetic field has been derived quite recently for liquid mixtures (Ig-

lesias T. P. & Reis J. C. R, 2016). Moreover, sensor solutions based on the measure-

ment of the complex permittivity has been suggested, such as, for the inspection of

contamination of motor oil (Perez A. T. & Hadfield M, 2011). Likewise, similar con-

cepts were applied for investigation of excess absorbance and the utilization of clas-

sical Lorentz oscillator model of binary liquid mixtures (Baranovic G, 2017). Further-

more, the study of terahertz time-domain spectroscopy of water and alcohol mixtures

(McGregor J., 2015), and the study of ionic liquid mixtures incorporated with quan-

tum mechanical interpretation by the concept of dampened harmonic oscillator (Mou

S., 2017). On the second hand, interesting studies on dynamic behavior of molecular

two-layered nanofilms have been studied in NIR region for accessing permittivity

and other optical properties of such structures (Rodic D., 2013; Setrajcic J. P, 2017).

For this research we utilize the idea of light dispersion of authentic diesel samples

namely diesel samples for varying climatic conditions, and kerosene. Then we study

the dispersion properties of fake fuels, this involves mixtures of various diesel sam-

ples and kerosene. We investigated dispersion properties by measuring transmission

spectrum in the Vis-NIR using spectrophotometer, and refractive index using Abbe

refractometer. In this thesis it is demonstrated that, the refractive index data obtained

using Abbe refractometer which is accurate table measurement device, and Vis-NIR

4

spectrophotometer, can be combined and used for novel type of diesel oils adultera-

tion detection by utilizing the excess permittivity property. For this purpose, the sin-

gly-subtractive Kramers-Kronig (SSKK) dispersion relation which is based on trans-

mittance data inversion is applied, in the calculation of wavelength-dependent re-

fractive index of liquids (Lucarini V., 2005). SSKK was investigated and applied in

several studies where its potential was demonstrated (Heijin S., 2017; Herrmann M.,

2012; Unuma T., 2011). In this thesis based on our knowledge, for the first time we

report on the application of Kramers-Kronig (KK) analysis, which is based on causal-

ity principle, to assess excess permittivity of adulterated fuels which are binary liquid

mixtures.

This dissertation reviews and discuss about liquid fuels as well as their adultera-

tion in chapter 2. The theory of light interaction with the roughened glass-fuel inter-

face, the wetting property of liquids (contact angle), Beer-Lambert’s law, complex

refractive index, the SSKK relations, complex excess permittivity, Lorentz-Lorenz

formula, as well as the modified ideal law of binary mixtures, are discussed in chap-

ter 3. In chapter 4, the mechanism of protype sensor signal measurements, training

set description, refractive index measurements as well as transmittance measure-

ments are presented (Paper I, II, III and IV). In chapter 5 we briefly describe the data

analysis methods such as extinction coefficient, real refractive index, excess permit-

tivity, imaginary excess permittivity (Paper II and III), and the method of volume

increase (Paper IV). Chapter 6 delves into the results and discussions concerning the

major findings of this thesis. There we deal with the results of the prototype of an

optical sensor (Paper I), results of extinction coefficient calculations, Wavelength de-

pendent refractive index, and excess optical properties (Paper II and III). Moreover,

the results of handheld refractometer method are also presented (Paper IV).

5

2 LIQUID FUEL ADULTERATION

Fuels are materials such as coal, gas or oil, which when burnt in the presence of air

or oxygen produces energy in form of heat or power. The material must contain one

or more combustible elements such as carbon, hydrogen, sulphur, and other compo-

nents for it to be classified as fuel. In its natural state fuel does not contain any usable

energy, for it to be useful it must undergo a transformation process from one form to

another. Like other matter fuels exist in three states, which are classified either as

solid fuel such as wood or coal, liquid fuel such as diesel oil, and gasoline or kerosene

and gaseous fuel such as liquified petroleum gas (LPG). We focus our attention on

liquid fuels which are also sub classified as natural, which means crude oils before

any processing or artificial, meaning manufactured or processed fuels.

The common fuels are the final products of crude oil refinery process, these in-

cludes gaseous fuels, liquid fuels, lubricants, solvents, waxes and asphalts. Crude oil

is a complex mixture of many hydrocarbon groups such as butane, pentane, propane,

methane and ethane. Moreover, crude oils contain other hydrocarbon groups

namely, as paraffinic, naphthenic and aromatic, which in most cases are the main

building blocks of organic industry (Chaudhuri U. R, 2016). It is necessary to separate

different components from the original crude oil to obtain a usable fuel. To achieve

this, crude oil must go through a refinery process which involve several stages that

must be undertaken in succession before the final fuel product is obtained these are,

distillation, thermal cracking, catalytic process, treatment, formulation and blending

(US Patent No. 2,914,457, 1959). The results of a refinery process are liquified petro-

leum gas (LPG), gasoline, naphtha, kerosene, aviation turbine fuel (ATF), diesel oil,

lubrication oils, in the respective order, and many other products (Chaudhuri U. R,

2016; Speight J. G, 2006; Chaudhuri U. R., 2011). The variation of refineries influences

the appearance and concentration of the end products, therefore chemical com-

pounds of final fuel products vary depending on geographic origin of crude oil. In

this work we focus our attention on diesel oil and kerosene whose mixtures represent

a difficult case of fuel adulteration.

2.1 DIESEL OIL

One of the many products of crude oil refinery process is diesel, which is also called

petrol diesel. This is necessary to different it from bio diesels, un like bio diesel, petro

diesel is a by-product of crude oil, which is obtained through fractional distillation

process with burning temperature ranges of 250˚C – 350˚C. In the refinery process

temperature is the chief method through which different components are extracted

and separated from the rest. The chemical composition of diesel is 75% hydrocarbons

which are mainly paraffins, and 25% aromatic hydrocarbons such as naphthalenes

and alkylbenzenes (Cooke R. A. & Ide R. H, 1985). For common diesel oils, the aver-

age chemical formula ranges from C10H22 to C15H32, and their densities has a range of

6

0.820−0.850 kg/l, this density variation imply a consequent refractive index variation

across the span of different diesel samples, especially those coming from different oil

fields as well as the differences in the refinery process (Aleme H. G., 2010). Diesel

fuel is more or less superior to gasoline because it is more efficient, much safer in

operation, and offers a wide range of performance as a transport fuel for many dif-

ferent types of engines. Literature claims that diesel oil contains 32-40 MJ/L of energy,

about 18% to 30% more energy per gallon as compared to gasoline (Murago E. N. M,

2013). Because of this diesel fuels are widely used in many types of transportation

vehicles, with the exception of the gasoline-powered passenger automobile (Aleme

H. G., 2010). In many developing countries the proportion of automobiles utilizing

diesel is larger compared to those utilizing gasoline, making it a lucrative target for

adulteration practices.

2.2 KEROSENE

There are two ways by which kerosene is obtained, the first one is from distillation

of crude oil under atmospheric pressure, and the second one is from thermal or steam

cracking of heavier petroleum streams. After this initial process further treatment is

necessary where by kerosene is treated by a couple of different processes to reduce

the level of materials such as sulphur, nitrogen and olefin (Klimisch H. -J., 1997). Pre-

dominantly kerosene contains C9 to C16 hydrocarbons with a boiling range of 145˚C –

300 ˚C, moreover, the density of kerosene varies from 0.760 − 0.800 kg/l. This implies

a consequent variation in refractive index for different kerosene samples. The intrin-

sic composition of kerosene is 70% paraffins and naphthenes, 25% aromatic hydro-

carbons and less than 5% olefins. Kerosene is mainly utilized for powering jet engines

of aircraft (jet fuel), and is also utilized in some rocket engines, never the less it is

utilized domestically for cooking and lighting fuel (US Patent No. 2,914,457, 1959).

The cost for kerosene is usually lower compared to diesel, this makes it lucrative

product for malpractice such as adulteration.

2.3 ADULTERATION

Adulteration is actually illegal practice which involves addition of unauthorized sub-

stances into pure products, this practice leads to a final product which does not com-

ply to specifications or standards, and products that might be harmful to humans or

to the environment (US Patent No. 2,914,457, 1959; Obeidat S. M., 2014). For the case

of diesel adulteration foreign substances such as kerosene, naphtha, as well as other

chemicals resulting or originating from petroleum refining processes are utilized as

adulterants for diesel oils. Furthermore, in certain cases the gasoline boiling range

hydrocarbons are added into automotive diesel, also west industrial solvents such as

lubricants or heavier fuel oils in small portions are added into diesel fuels (Murago

E. N. M, 2013; Cunha D. H., 2016). Likewise, other adulterants for diesel oils are bio-

diesel, vegetable oil, residual oil and sulphur (Majhi A., 2012; Flumignan D. L., 2010).

7

Among these, kerosene is highly preferred because, kerosene and diesel both have

overlapping hydrocarbons namely, C10 to C15 for diesel and C9 to C16 for kerosene

(Tharby R, 2002; Mendes G. & Barbeira P. J. S, 2013; Pedrosso M. P., 2008; Dey R. &

Dwidevi A, 2014). In areas where adulteration practice is prevalent 10% to 15% adul-

teration is usually profitable while adulteration level below 10% becomes less finan-

cially attractive. Moreover, the adulteration levels above 30% can easily be detected

due to its direct impact on the emitted gases, and because of malfunction of engines.

Despite this fact, adulteration even below 10% is still practiced. In recent years, major

techniques for screening adulterated fuel have been unable to detect adulteration

levels below 20% (Mishra V., 2008). It is therefore necessary to consider adulteration

at 5%, 10% and 15%, and this we have done in this thesis.

Fuel adulteration practice in different parts of the world is based on very similar

reasons such as greed caused by different tax systems for different fuels where, ker-

osene is always subsidized by the government in the attempt to lower its price for

poor population. Gasoline is taxed highest followed by diesel, while kerosene is

taxed least (Gupta A. & Sharma R, 2010). On the other hand, profit making in oil

business is also one of the major reasons for adulteration practice (Sinha S. N., 2005).

Despite these reasons lack of monitoring and consumer awareness is also the major

reason for adulteration, because similarities in properties of these petroleum prod-

ucts make it difficult for lay individuals to notice the difference when adulterated.

Moreover, the lack of transparency and noncontrolled regulations especially in pro-

duction, supply and market chains and lack of simple equipment for detection and

identification of fake fuels (Murago E. N. M, 2013).

Fuel adulteration has negative impacts which are widely visible in our daily lives,

the environment surrounding us, and economy. These include pollution crisis, res-

piratory infections and more poisonous exhaust gases (Taksande A. & Hariharan C,

2006). Moreover, there are several ambient air pollutants such as SO2, NO2, particu-

late matter (PM), polycyclic aromatic hydrocarbons (PAHS), as well as volatile or-

ganic compounds (VOC), which are emitted from automobile exhaust and industrial

activity. These pollutants lead to morbidity and mortality especially in developing

and third world countries (Sinha S. N. & Shivgotra V, 2012; Sinha S.N., 2010; Roy S,

1999). In some cases when high boiling compounds are utilized as adulterants in fuel,

it causes increase in knock, engine wear and in certain circumstances engine starting

problems. On the other hand, when low boiling point compounds are applied as

adulterants it leads to vapor lock. The other serious effect for governments is tax eva-

sion (Payri R., 2013). Furthermore, adulteration leads to pollution, poor engine per-

formance, failure of machine components, lower returns for buyer’s money, and de-

crease of the availability of kerosene to the needy, especially poor communities

(Mishra V., 2008). On the other hand, fuel adulteration may lead to environmental

hazards, increase in tail pipe emission, ill effects on public health, and engine mal-

function (Obeidat S. M., 2014). Finally, nonconformity resulting from adulteration of

fuel can cause damages that are difficult to repair in cars, these includes engine’s

8

sudden drops, possibility of traffic accidents, over fuel consumption and more emis-

sion of exhaust gases as well as particulate matter (Cunha D. H., 2016).

To control the problem of fuel adulteration, monitoring of fuel quality is inevita-

ble especially at different distribution chain stages. In developing countries this is

even more difficult because there are multiple importers and distributors of fuel,

therefore complicating the process of monitoring the quality of fuel. The task of fuel

quality monitoring can be accomplished either by taking the sample to the laboratory

for measurements, or by performing field screening using portable equipment. Many

existing methods for fake fuel screening are meant for laboratory purposes, however,

few methods do exist that can be used for field measurements. The challenge with

field measurement is the lack of more portable and cheaper equipment, especially for

end consumers like car drivers who have little or completely lack knowledge about

neither physics nor analytical chemistry.

Currently several methods have been deployed by regulating agencies for adul-

teration detection, among them are, test based on evaporation (ASTM D380), based

on distillation (ASTM D86), based on gas chromatography (GC), by using fiber optics

sensor, and using ultrasound. This is based on American Society of Testing and Ma-

terials international (ASTM) (Gupta A. & Sharma R, 2010; Vogt T. K., 2004; Teixeira

L. S., 2008). Other methods for laboratory and field screening include near and mid

infrared spectroscopy NIR and MIR, Fourier transform infrared spectroscopy FTIR

(Roy S, 1999; Vogt T. K., 2004; Gupta A., 1992), filter based method, test based on

specific gravity, viscosity, odour test, ultrasonic, and titration techniques (Sadat A.,

2014) (Bhatnagar V, 1981; Shahrubahari M., 1990; Perreira R. C., 2006). Furthermore,

methods such as synchronous fluorescence scan (SFS) (Sinha S.N., 2010), by measur-

ing refractive index (Ariponnammal S., 2012), by FTIR spectra coupled with principle

component analysis (PCA) as well as linear discriminant analysis (LDA)(Perreira R.

C., 2006). Also, by gas chromatography and gas chromatography spectroscopy

(Moreira L., 2003), by using photothermal detection method (Lima J., 2004), by filter

paper method (Majhi A., 2012), by using excitation emission matrices fluorescence

spectroscopy (EEMF), and by multiway principle component analysis (Obeidat S. M.,

2014; Patra D. & Mishra A, 2001), moreover, it is done by using optical sensors (Sri-

vastava A., 1997), and finally by fiber optics sensors (Eriksson M. & Iqbal Z, 2014).

There are several measurement methods that deal with portable liquid measure-

ments such as, mobile phone based optical sensing (Felix V. J., 2015), by using porta-

ble electronic techniques (Wiziack N. K., 2011), by image processing method (Ram-

mohan V. M, 2010), and by using a chemical sensor array (Cozzolino D., 2006).

With respect to adulteration detection policy, several standards such as ASTM

D4052, ASTM D3810, ASTM D86, and ASTM D842, exist to facilitate the fuel adulter-

ation detection both in US and Europe (Gupta A. & Sharma R, 2010). Some of the

specific fuel compositions that are tested by the standards are cetane number (ISO

5165, EN15195, EN16444 and ASTM D613). In addition to this cetane index is also

tested (ISO 4264/ASTM D976). Consequently, the density tests are (ISO 3675/ISO

12185), aromaticity test (EN 12916, ASTM D 1319), testing the content of Sulphur (ISO

9

20846), testing flash point (ISO 2719/ASTM D93), testing kinematic viscosity (ASTM

D445/ISO 3104) (Bhanu P., 2017). However, these standards cannot be adopted in the

entire world for checking adulteration, the reason being that, petroleum products are

mixtures constituting of several polycyclic aromatic compounds (PACS). The com-

position of these compounds is affected by the origin of oil field, which vary from

one location and geographic origin to the other. Because of this, it is necessary to

enact standards for different countries, however, this has not been achieved.

10

3 THEORY

This thesis studies the optical properties of diesel oils and kerosene, and thereafter

use these properties in data analysis, to identify and possibly quantify the level of

adulteration. This is possible if we consider the interactions between electromagnetic

radiations and the liquid samples by optical spectroscopy. Optical spectroscopy

measures the spectral distribution of electromagnetic radiation(light) analytically.

The electromagnetic radiation is very broad, it spans all the way from gamma rays

(10-4 - 10-1nm) to microwaves (1 mm-100 cm). The spectral region between 380 nm and

800 nm represents the visible part of electromagnetic spectrum which is sensitive to

our vision system, while the region spanning from 700 nm to 3000 nm is the near-

infrared region (Nicolai B. M., 2007). Analysis of NIR spectra is characterized by low

molar absorption as well as less scattering, which enables evaluation of pure materi-

als with less effort. It is interesting that although it was discovered by Herschel as

early as 1800, it was initially ignored by the scientists, who thought it lacked analyt-

ical capabilities (Roggo Y., 2007; McClure W. F., 2003; Wiedemann L. S. M., 2005).

Despite that, in recent years NIR spectral region has become dominant especially in

molecular spectroscopy, thanks to powerful and inexpensive computers. The com-

puters can give quantitative information on the major organic components and func-

tional groups of fuels such as hydrocarbons (Takeshita E.V., 2008). Recently the sig-

nificance of NIR region especially for fuel studies has become popular for determi-

nation of the quality of liquid fuels. This it achieves through determination of the

octane number, ethanol contents, methyl tetra-butyl ether (MTBE) content, distilla-

tion points, aromatic as well as saturated contents, and Reid vapor pressure. Reid

vapor pressure is the absolute vapour pressure exerted by a liquid at 100˚F, it is a

common measure of volatility of gasoline as determined by the test method ASTM-

D-323 (Teixeira L. S., 2008; Rammohan V. M, 2010; Barbeira P. J. S, 2002; Wooten F,

2013; Wiener O, 1912).

When photons of varying wavelengths strike the matter, the transmission prop-

erties of the medium are influenced by reflection, scattering, refraction as well as ab-

sorption. Part of the light incident on the material may be deflected back to the direc-

tion from which it come, that is called reflection. Part of light might be lost within the

material due to interaction with molecules in the material, which leads to specific

electron transition in the material, or the lost energy may be converted to heat, this is

known as absorption. Part of incident light might end up being deflected randomly or

even spread randomly in all directions this is the so-called scattering, while the light

that emerges to the other side is the transmitted light (Nicolai B. M., 2007). For the case

of diesel oils and kerosene major part of light is transmitted to the other side because

the fuels are highly transparent. Moreover, the phenomenon of refraction is exhibited

at the interface between liquid and cuvette which further leads to total internal reflec-

11

tion and scattering. This takes place due to non-absorbing characteristics of these sam-

ples. Finally, the other challenge to worry about is the cuvette reflections, which we

have dealt with rigorously in chapter 4.

3.1 LIGHT INTERACTION WITH THE ROUGHENED GLASS-FUEL INTERFACE

Our first publication (Paper I) was solely devoted to report on practical experimental

aspects rather than theory. Therefore, in this section a qualitative theoretical picture

about a stable rough glass-liquid system is given, followed by liquid drop spreading

which is unstable system. Firstly, several phenomena that are present during the pro-

cess of light interaction with the roughened glass-fuel interface are considered. These

includes the effects originating from the nature of incident light, and the effects re-

sulting from the surfaces in contact, namely fuel and roughened glass. Secondly, phe-

nomena that affect the time development, which is one indicator of the drop spread-

ing and which affect the scattering of light thus influencing the measured signal are

described. 3.1.1 Description adopted from the pigment model

The interaction between electromagnetic radiation with roughened glass-fuel inter-

face is a rather complex phenomenon. For a better understanding of the complex

processes taking place, the theoretical setup utilized by (Niskanen I., 2010; Beckmann

P., 1963) is adopted, with slight modification of replacing a pigment with roughened

glass window. In the setup the pigment is transparent, same case holds for a rough-

ened silica glass, moreover, the pigment surface is considered rough, same case holds

for the roughened glass window.

If a light ray strikes the interface between roughened glass and liquid, it will

always be scattered from the glass provided that, there is a mismatch of refractive

index between the fuel and rough glass. The scattering process is a complex phenom-

enon since it incorporates both reflection, refraction, and diffraction phenomena

which are inseparable in the process (Niskanen I., 2012; Nussbaumer R. J., 2005). In

the theory it was assumed that, the plane wave is incident on the suspension. How-

ever, in this thesis the case of handheld device is qualitive because the laser beam is

focused and not a plane wave. Let us consider a hypothetically oversimplified model

in Fig. 3.1.

12

Figure 3.1. Optical paths of transmitted and refracted rays (Niskanen I., 2012).

From (Fig. 3.1), the optical pathlength difference between the two rays is given by

(nglass – nliquid) z, where nglass and nliquid are the refractive indices of glass and liquid respec-

tively, and z is the separation between the two surfaces. This difference causes the

phase shift in the electric field between the two light rays, provided that the glass size

is larger in comparison to the wavelength of incident light. The resulting phase shift

is given by:

∆𝜑 =2𝜋

𝜆(𝑛𝑔𝑙𝑎𝑠𝑠 − 𝑛𝑙𝑖𝑞𝑢𝑖𝑑)𝑧, ( 3.1)

where λ is the wavelength of light. Next, upon further mathematical manipulations

(Niskanen I., 2012), one arrives at the expression for intensity of transmitted light

which is detected at the far field region. This is given by the power density function

as;

𝐼 = |𝐸02 ∫ 𝑤(𝑧) 𝑒𝑥𝑝 {𝑖 [

2𝜋𝑐𝑜𝑠𝜃

𝜆(𝑛𝑔𝑙𝑎𝑠𝑠 − 𝑛𝑙𝑖𝑞𝑢𝑖𝑑)] 𝑧}

∞

−∞

𝑑𝑧|

2

, (3.2)

where E0 is the electric field amplitude of incident light. Next, if Eq. (3.2) is taken

further by determining its Fourier transform, the final equation is a function with a

Gaussian shape. Moreover, if the distribution of the transmitted light follows the

Gaussian distribution, the final resulting equation is given as:

𝑇 = 𝐼𝐼𝑂

⁄ = exp {− [2𝜋𝑐𝑜𝑠𝜃

𝜆(𝑛𝑔𝑙𝑎𝑠𝑠 − 𝑛𝑙𝑖𝑞𝑢𝑖𝑑)]

2

}, (3.3)

where IO and I are the incident and transmitted light intensities respectively.

13

According to (Niskanen I., 2010), the treatment above strongly resembles light

scattering from a rough surface that is subjected to the statistics of a Gaussian surface,

and therefore, from this observation it is logical if we conclude that, the light scatter-

ing obeys the gaussian distribution (Beckmann P., 1963). Moreover, since absorption

is very low in diesel oils, it is possible to obtain the reflectance by R=1-T, which is the

first approximation. The real phenomenon taking place is rather complex because of

back scattered light which is reflected from the smooth side of the rough glass, lead-

ing eventually to multiple scattering and transmission, thus the model is only quali-

tative. The simple treatment of R=1-T implies that, reflectance also obeys the Gauss-

ian distribution properties. The average surface roughness of the roughened glass

utilized in this work was Ra = 0.48 μm which was measured by a stylus profilometer,

the surface was finished by diamond grinding pads with criss-cross surface finishing

style which obeys gaussian distribution according to (Tanner L. T, 1976; Whitley J.

Q., 1987; Richard B. Z, 1983; Wolfgang S. & Nico C, 2011). This is very important,

because it leads us to expect normally distributed signals resulting from back scat-

tering. On the other hand, as the difference or variation of refractive index becomes

high between the liquid and glass, the scattering signal also becomes high and vice

versa (Paper I).

Facet model along Gaussian surface height distribution was investigated for

transmission of light through a roughened glass slide immersed in liquid (Nuss-

baumer R. J., 2005).

3.2 THE WETTING PROPERTY OF LIQUIDS (CONTACT ANGLE)

3.2.1 Ideal wetting process

In the liquid-glass interface, another very important factor which affects the scatter-

ing of light is the wetting which dictates the contact-angle between the liquid and

solid. There are two types of wetting processes namely hydrophilic, when the liquid

and the glass come into contact spontaneously resulting into a film, and hydrophobic

where there is no any contact between liquid and glass. The basic laws of wetting

were first developed by Laplace and Young, for solids which are ideal, namely

smooth solid surfaces (Quere D, 2008).

According to Laplace and Young, the material surface carries a certain amount of

energy known as surface tension which is denoted by γIJ representing energy per

unit area for an interface with varying phases I and J. If γ is the surface energy (sur-

face tension) at the interface between liquid and air, the two leads to a relation which

was first imagined by Marangoni (Quere D, 2008), the relation considers the spread-

ing of a film as demonstrated in Fig. 3.2(a). The relation of Marangoni is known as

spreading parameter (S) which is given by S= γSA – γSL – γ, where γSA and γSL are the

surface tensions between the solid-air, and solid-liquid interfaces respectively.

14

Figure 3.2. Two classical wetting situations for an ideal material (Quere D, 2008).

It is the sign of parameter S that dictates the behavior of the liquid drop on the

surface of the solid, if S> 0 spreading takes place, while for the opposite case a lens

like shape is formed which does not spread. This opposite case is demonstrated in

Fig. 3.2(b), where unlike for the case of (Fig. 3.2(a)) here a contact angle β exist be-

tween the solid and liquid drop. Moreover, the equilibrium condition is achieved by

the liquid drop on the contact line due to the actions of varying surface tensions. The

balance equation for such equilibrium condition is given as:

𝛾𝑆𝐴 = 𝛾𝑆𝐿 + 𝛾𝐶𝑂𝑆𝛽 , (3.4)

this theoretical model is only valid for smooth surfaces (Quere D, 2008).

3.2.2 Contact angle hysteresis

Most glasses are said to be naturally rough, even those glasses which looks smooth

to our eyes are usually rough at micrometric dimensions. There are several reasons

for the roughness, these includes lamination process during fabrication which gen-

erates micro grooves, grain compaction a process which results into roughness of

grains, as well as coating (Quere D, 2008). According to (Quere D, 2008), the contact

line on the solid surface can be pinned due to surface defects, consequently causing

drops on an inclined plane to either remain stationery or causing wetting and non-

wetting defects on both sides of the surface (Furmidge C. G. L, 1962). This leads to

asymmetry in contact angle which causes pressure to build up. The built-up pressure

further leads to a force which is capable of resisting gravity especially when the drop

is small. Therefore, both the diversity in chemical composition of the material and

normal surface roughness affects the contact angle.

Generally, the contact angle depends on the previous status of liquid deposition

on the surface. Provided that the drop was deposited gently, it will continue to

spread and will only stop due to new wetting defects. After some time, evaporation

will take place and will change the configuration to resemble that of a pinned drop

as illustrated in Fig. 3.3.

15

Figure 3.3. Illustration of the apparent pinning of a contact line on an edge (Quere D., 2008).

According to basic laws of wetting developed by Laplace and Young, when the

liquid meets the solid surface, the contact angle β is formed. It is possible for β to

assume various values between β and ℼ-ɸ + β at the edge of the solid, where ɸ is the

interior angle between the two surfaces forming an edge. If the horizontal is consid-

ered as the reference, the contact angle hysteresis can be quantified. Contact angle

hysteresis phenomenon should be considered for positive reasons such as, to assist

in guiding the flow of liquid along a specified line of defects. This will allow the liq-

uid to follow a specific path. On the contrary this phenomenon can be detrimental,

for instance when water droplets remain stationery on glass surfaces leading to their

deterioration (Quere D, 2008; Furmidge C. G. L, 1962).

3.2.3 Behavior of liquid over a rough surface

Unlike smooth surfaces, the contact angle is a local quantity for rough surfaces. Pro-

vided the surface gradient is very minimal in comparison to the angle of contact β,

the spreading of liquid over the glass surface is not influenced by roughness. To the

contrary, when the gradient is significant which is usually the case for spreading

drops, the spreading rate is altered (Raltson J., 2008). The rate of drop spreading on

rough surfaces changes with time. The changes are described by two regimes and

defined by shape of drop as it spreads over the surface, namely, the vary fast region

where the drop assumes the shape of a Mexican-hat (MH), where the drop has a cap

and a foot, and the vanishing of the cap as it is consumed by the foot. Mathematically,

in the MH region, the rate of change of the spreading rate is given by:

𝛥𝑅 (𝑡) = √𝐷𝑡, (3.5)

where D = C/ η is the coefficient of diffusion, 𝜂 is the viscosity, and C is a property of

the surface proportional to roughness height, and t, is the time. The growth of the

foot then reduces the spreading rate to

𝑅 (𝑡) ≈ √𝑡4

, (3.6)

16

3.3 BEER-LAMBERT’S LAW

In optical transmission spectroscopy the most important law governing the interac-

tion of light and liquid is the Beer-Lambert’s law, especially for homogeneous and

isotropic liquids such as diesel oils and kerosene. If the beam of light with intensity

IO strikes the sample and get transmitted through the sample, emerging to the other

side with intensity I, the extent of absorption of light intensity in the sample can be

treated with the equation:

𝑑𝐼

𝑑𝑥= −𝛼𝐼(𝑥), (3.7)

by integrating Eq. (3.7), we obtain the wavelength-dependent Beer-Lambert’s law

given as:

𝐼(𝜆) = 𝐼𝑂 (𝜆)𝑒−𝛼(𝜆)𝑑 , (3.8)

where α, λ and d represents the absorption coefficient, wavelength of incident light,

and sample thickness respectively. If the absorption coefficient is represented in

terms of wavelength, the resulting equation becomes:

𝛼(𝜆) =1

𝑑𝑙𝑛

1

𝑇(𝜆) , (3.9)

where T = I/IO is the transmittance. The schematic diagram depicting light matter in-

teraction process is illustrated in Fig. 3.4.

Figure 3.4. Schematic illustration of Beer- Lambert’s Law.

17

3.4 COMPLEX REFRACTIVE INDEX

In cases when the medium exhibits both absorption and refraction, it is possible to

represent both quantities with the so called complex refractive index (N) (Wooten F.,

2013). This quantity is represented in terms of the real and the imaginary refractive

indices n and k respectively. The wavelength dependent complex refractive index is

given by Eq. (3.10) as:

𝑁(𝜆) = 𝑛(𝜆) − 𝑖𝑘(𝜆), (3.10)

where i is the imaginary unit, n can be measured by several methods. However, in

this work the measurement was done by an abbe refractometer. The relationship be-

tween the extinction coefficient k and the absorption coefficient α is given by:

𝑘(𝜆) =𝛼(𝜆)𝜆

4𝜋. (3.11)

3.5 SINGLY SUBTRACTIVE KRAMERS-KRONIG RELATION (SSKK)

In optical spectroscopy, the refractive index of a particular sample at a specified

wavelength cannot be obtained directly based on transmittance measurement. How-

ever, this quantity is directly related to the density of liquid samples. Moreover, by

Abbe refractometers it is usually possible to find the value of refractive index at a

single specified wavelength. Therefore, to find this value at different wavelengths, it

is imperative to utilize the absorption and dispersion phenomena which are usually

related. In linear optical spectroscopy light absorption and dispersion is guided by

the causality principle. Therefore, it is usually possible to estimate one quantity pro-

vided that the other is known, namely absorption and dispersion. These quantities

are connected by a pair of equations called Kramers-Kronig (KK) relations (Peiponen

K. -E. & Saarinen J. J, 2009; Ahrenkiel R, 1971; Peiponen K. -E., 1998).

If finite range integration is desired, the conventional K-K relation is modified to

include the anchor point obtained by the Abbe refractometer, result of such modifi-

cation is the SSKK. We use this relation to extrapolate data especially in refractive

index calculations. The significance of SSKK comes from the fact that, devices utilized

for refractive index measurements, such as Abbe refractometer, give the refractive

index reading only under one wavelength. Therefore, we can extrapolate the values

of refractive indices over a wide wavelength range by using SSKK (Peiponen K. -E.,

1998). In this thesis the wavelength of Vis-NIR radiation is exploited rather than an-

gular frequency. Because we can measure a discrete refractive index value for an an-

chor point with the Abbe refractometer, we further utilize SSKK dispersion relation

to find out n as:

18

𝑛(𝜆′) − 𝑛(𝜆′′) =2(𝜆′2 − 𝜆′′2)

𝜋𝑃 ∫

𝜆𝑘(𝜆)𝑑𝜆

(𝜆2 − 𝜆′2)(𝜆2 − 𝜆′′2)

∞

0

, (3.12)

where n(λ’) is the wavelength-dependent refractive index, n(λ’’) is a priori known

refractive index value at an anchor point λ’’ and P denotes a constant known as the

Cauchy principal value. For practical purposes, the integral especially in the case of

SSKK analysis, can be usually truncated to correspond to the initial and final point

of the measured transmittance. Here we use the method of optimal choice of the an-

chor point λ’’ to minimize possible data inversion error. This method is based on the

properties of a Chebyshev polynomial as shown by Palmer et al (Palmer K. F., 1998).

We are not free to choose the anchor point because of the fixed wavelength of the

Abbe refractometer. Nevertheless, we obeyed a novel approach by the choice of the

optimal anchor point by fixing the wavelength of the final point (λf), of the transmit-

tance spectrum and we calculated the initial wavelength (λi) that corresponds to the

choice of the optimal anchor point. The wavelength λi is then obtained from a zero

of a Chebyshev polynomial of the first kind as follows (Palmer K. F., 1998):

𝜆𝑖 =𝜆𝑓𝜆′′

√2𝜆𝑓2 − 𝜆′′2

. (3.13)

Therefore, we initiated scanning of transmittance spectrum from this calculated

wavelength. The SSKK method has been used, e.g., to analyze properties of diesel

oil droplets (Dombrovsky L. A., 2003). In the case of SSKK relation the choice of the

location if a single anchor point usually is not a critical factor for successful inversion

of the extinction data (Paper II and III).

3.6 COMPLEX EXCESS PERMITTIVITY

In this study we deal with binary liquid fuel mixtures. We denote relative permittiv-

ity of diesel oil by εD and corresponding permittivity of kerosene by εK. According to

the definition, and allowing losses (Iglesias T. P. & Reis J. C. R, 2016), the excess rel-

ative permittivity in our case is given by the expression:

εE = ε − ε𝑖𝑑𝑒𝑎𝑙 = ε − 𝜀𝐾 [1 + fD ((𝜀𝐷

𝜀𝐾

) − 1)], (3.14)

where ε is the measured permittivity, εideal is the permittivity of binary mixture with-

out any interaction processes between the mixture components, fD is the volume fill

fraction of diesel oil present in the mixture, and the permittivity ratio in the paren-

thesis is known as permittivity contrast (Papers II, III and IV).

19

The ideal permittivity concept assumes that there is no chemical activity when-

ever two different liquids are mixed, some liquids fulfil this requirement. However,

due to the presence of chemical activity in some liquids, there will be intermolecular

interactions which will contribute to the molecular polarization of electric charges,

causing changes to optical properties. Eventually, the ideal permittivity is the same

as the upper Wiener bound (Wiener O, 1912) of an effective medium which is given

as:

𝜀𝑖𝑑𝑒𝑎𝑙 = 𝑓𝐷𝜀𝐷 + (1 − 𝑓𝐷)𝜀𝐾 . (3.15)

In general case if the permittivity is a complex number, then the excess permittiv-

ity is also complex. The frequency dependent complex permittivity in a homogene-

ous medium as a function of wavelength is given by the expression:

ε(𝜆) = 𝜀1(𝜆) + 𝑖𝜀2(𝜆) = 𝑛2(𝜆) − 𝑘2(𝜆) + 𝑖2𝑘(𝜆)𝑛(𝜆).

(3.16)

The complex relative permittivity of a medium is given by the relation ε = N 2, this

was utilized in (Eq. (3.16)) and in (Papers II, III and IV).

Next, by utilizing Eqs. (3.14) and (3.16), the equation for imaginary excess permit-

tivity is given by:

𝐼𝑚𝜀𝐸(𝜆) = 2𝑛(𝜆)𝑘(𝜆) − 2𝑛𝑖𝑑𝑒𝑎𝑙 (𝜆)𝑘𝑖𝑑𝑒𝑎𝑙(𝜆) =

2𝑛(𝜆)𝑘(𝜆) − 2(𝑓𝐷𝑛𝐷(𝜆)𝑘𝐷(𝜆) + (1 − 𝑓𝐷)𝑛𝑘(𝜆)𝑘𝑘(𝜆)),

(3.17)

where n(λ) and k(λ) are values of adulterated samples, while in our case nideal(λ) and

kideal(λ) are the values of ideal binary mixtures, nD(λ) and kD(λ) are the values of au-

thentic diesel oil, and nK(λ) and kK(λ) are the values of kerosene respectively. All these

were obtained using the measured refractive indices and the transmitted data inver-

sion using SSKK (Paper III).

3.7 LORENTZ-LORENZ FORMULA

The Lorentz oscillator model is the highly preferred formula for calculating the re-

fractive index of binary mixtures, such as adulterated diesel oils. This is given in (Bar-

anovic G, 2017), and is expressed as:

𝑛2−1

𝑛2+2= 𝑓𝐷

𝑛𝐷2 −1

𝑛𝐷2 +2

+(1−𝑓𝐷)(𝑛𝐾

2 −1)

𝑛𝐾2 +2

, (3.18)

where n is the refractive index of the resulting mixture, nD is the refractive index of

diesel oils, and nK is for kerosene. We can define an ideal mixture using this model

20

because, it gives good estimate of volume fill fraction for ideal mixtures when there

are no interactions between molecules of the participant liquids. However, when the

interactions exist, this formula gives erroneous volume estimates (Paper IV).

3.8 MODIFIED IDEAL LAW OF BINARY MITURES The ideal mixture equation (Eq. 3.16) can be re-written so that the total volume ex-

pression includes the volumes of the individual binary mixture constituents, namely

diesel oil as well as kerosene. This leads to a modified equation given as:

𝜀𝑖𝑑𝑒𝑎𝑙 =𝑉𝐷

𝑉𝜀𝐷 +

𝑉𝐾

𝑉𝜀𝐾 , (3.19)

where V = VD + VK is the total volume of the mixture.

Next, Eq. (3.16) is further modified to incorporate the novel concept of increase of

volume of pure diesel oil in the suspected sample. This was achieved by introducing

another variable V’, this leads to the modified formula given by:

𝜀𝑖𝑑𝑒𝑎𝑙(𝑉′) =𝑉𝐷+𝑉′

𝑉+𝑉′𝜀𝐷 +

𝑉𝐾

𝑉+𝑉′𝜀𝐾. (3.20)

The added volume V’ should be with respect to the magnitude of the initial vol-

ume V of the suspected sample. If the volume V’ is continuously increased, the sam-

ple approaches the case of ideal mixture as the concentration of kerosene continues

to diminish. The limiting value for this mixing procedure is given by Eq. (3.21) (Paper

IV).

lim𝑉′→∞

𝜀𝑖𝑑𝑒𝑎𝑙(𝑉′) = 𝜀𝐷. (3.21)

21

4 OPTICAL MEASUREMENTS

In this chapter we briefly describe the mechanism behind signal measurement

method of the prototype sensor, this is followed by description of the samples that

were utilized in this thesis. Thereafter, the refractive index data which were meas-

ured both in Finland and Tanzania are presented. Finally, the transmittance meas-

urements for the samples are presented. These were necessary for calculations and

computations in the next parts.

4.1 OPTICAL SIGNAL MEASUREMENT (PROTOTYPE)

Optical sensors provide quick means to solve complex measurement problems, such

as the case of diesel oil adulterated by kerosene. Herein, a prototype sensor for de-

tection of adulteration, which is a modified version of a handheld gloss meter (Kui-

valainen K., 2010), is presented (Paper I). The theoretical background of light inter-

action with rough glass-liquid interface, which was adopted from (Niskanen I., 2012;

Beckmann P., 1963; Nussbaumer R. J., 2005), was presented in chapter 3. This is fur-

ther complemented by the theory of wetting process and refractive index mismatch

which are well addressed in several studies (Nussbaumer R. J., 2005; Quere D, 2008;

Furmidge C. G. L, 1962; Raltson J., 2008; Cazabat A. M. & Cohen S. M. A, 1987).

For mixtures of less problematic samples such as sample A and D, the table model

Abbe refractometer give accurate results which makes it easy to separate the samples.

However, for field measurement conditions such a bulky device is not practical. To

the contrary there do exist a handheld Abbe refractometer (Atago, H-50), which was

tested for measurement but offers poor accuracy. However, in the later study a

method that can make use of a handheld refractometer to predict and separate highly

adulterated fuels from low adulterated fuels was developed (Paper IV). The pre-

sented prototype relies on combined effect of roughness, contact angle, wetting, and

refractive index mismatch. These effects cause different liquids to behave differently

on the rough glass surface, enabling the identification and separation of signals rec-

orded from different samples with high accuracy.

The other stimulus for this work, was a recent article wherein the handheld

gloss meter was utilized for screening fake antimalarial tablets (Bawuah P., 2017),

thanks to the diffractive optical mechanism incorporated with the sensor. Similar dif-

fractive optical mechanism together with laser transmission have also been utilized

in other related works (Silvennoinen R., 1999; Jääskeläinen A., 2000). The sensor lay-

out is presented in in Fig. 4.1, and the light source of the sensor is a semiconductor

laser with an output power of 0.8 mW and which is lasing at 635 nm. Both the DOE

as well as the laser are inside the device depicted in Fig. 4.1. The rough quartz glass

which was adopted for this work is (VWR microscope slide ECN 631-1550) whose

refractive index value at 635 nm is 1.4570. For more detailed description of the pro-

totype refer (Paper I).

22

Figure 4.1. Schematic diagram of a handheld sensor for fake diesel oil screening. DOE is a diffractive optical element. The dashed lines describe scattering of light due to the surface roughness and refractive index mismatch between the fuel and the glass (Paper I).

4.2 DESCRIPTION OF THE TRAINING SET

One of the challenges faced by researchers in various fields is the issue of sampling,

namely choosing acceptable samples whose results can be representative of the pop-

ulation of the study. In this work, the issue was considered critically and as a result

both summer and winter categories were included, to represent the varying climatic

conditions across the globe. We utilized one summer diesel oil grade (sample A), and

two winter diesel oils (sample B, and sample C). The difference between winter diesel

oil samples is based on the lowest temperature that is specified for the car to operate,

namely the lowest temperature at which the engine should start. For sample B it is (-

20˚C) and for sample C it is (- 32˚C). The origin of crude oil for both summer and

winter diesel oil samples is Russia. Moreover, we utilized a well-known brand of

kerosene as an adulterant (Alfa Aesar, Haverhill, MA, USA), this is available for sci-

entists across the globe.

Adulterations were prepared by blending pure diesel oils (sample A, B and C)

with kerosene (sample D). Typical adulteration level across the globe is 20%-30%

(Mishra V., 2008), therefore, in this work lower percentages of 5%, 10% and 15% were

selected and utilized for analytical purposes. These samples were measured in the

laboratory in Finland.

The laboratory measurements in Finland alone were not enough, because fuel

adulteration is a global challenge which highly affect third world and developing

countries, more than developed countries. Based on this reason, we performed field

measurements in Tanzania. The samples for field measurements were provided by

fuel regulatory authority of Tanzania (EWURA). This was also necessary to enable

23

the study of variations in diesel oil and kerosene samples. Usually these samples

have varying properties based on oil fields of origin, because these liquids constitute

hundreds of hydrocarbons with different variabilities across the globe (Szymkowice

P. G & Benoges J, 2008). Moreover, these variations are also depicted in the refractive

index values of these liquids, which highly relies on the crude oil of origin.

Differentiating diesel oils and kerosene mixtures is difficult, since both fuels have

overlapping fingerprints in NIR spectral region. The sets A-D serve us as a “training

set” for exploring optical properties of diesel oil grades and kerosene, and thereafter

the data obtained with the aid of the training set can be used for designing practical

sensors for field conditions, and relevant software, to identify any adulterated diesel

oil product.

4.3 REFRACTIVE INDEX MEASUREMENTS IN FINLAND

Refractive index is a constant which describes or depicts the way in which light in-

teracts with the medium. It considers other external factors such as temperature as

well as pressure, which are not captured by the density measurements. This quantity

is very useful for characterization, namely in cases where two samples have varying

refractive index values, it is possible to separate them. Refractive index has been uti-

lized for characterizing samples in different studies (Payri R., 2013; Geacai S., 2012;

Polynkin P., 2005; Kim C. -B. & Su C. B, 2004; Magnusson R., 2010; Fernandes V. H,

2008; Mishra V., 2008; Ariponnammal S., 2012). However, there are circumstances

when the mixtures are problematic such as adulterated diesel oils, and the difference

is not obvious. Nevertheless, the refractive index at one wavelength (anchor point)

can be utilized, to indirectly extrapolate optical properties of the same material at

other wavelengths. This provide more possibilities for differentiation of the samples.

In this thesis Abbe refractometer (Atago RX5000) was used for measuring the re-

fractive index of different fuel samples at 589 nm, the device measures the refractive

index to an accuracy of ±0.00004. Table 1 shows the measured values for both authen-

tic and adulterated samples under room conditions (23˚C). From the third column of

Table 1, the magnitude of refractive index for summer diesel oil sample A is different

from those of winter diesel oil samples B and C. Moreover, for samples B and C there

is only slight difference in the third decimal, whereas the values for samples B and

C, are very close to that of sample D, making a mixture of these samples and kerosene

a problematic case.

24

Table 1: Refractive index of different authentic and adulterated fuel samples.

Sample Volume percentage of

kerosene

Refractive index for authentic samples

(589 nm)

Refractive index for adulterated samples

(589 nm)

Summer diesel oil (A) 1.46353

5%

1.46237

10%

1.46232

15%

1.46107

Winter diesel oil (B) 1.44775

5%

1.44894

10%

1.44863

15%

1.44791

Winter diesel oil (C) 1.44653

5%

1.44742

10%

1.4472

15%

1.44702

Kerosene (D) 1.44230

In Table 1 (fourth column) also are shown the refractive index data for samples A, B

and C, adulterated by different volume levels of kerosene, as measured by Abbe re-

fractometer. For sample A there is decrease in refractive index as the volume of adul-

terant (kerosene) increases, also the refractive index difference between 5% and 10%

adulteration is in the fourth decimal, while the difference between 15% adulteration

and the former is in the third decimal. Moreover, the values of refractive index for

the mixtures lies between the refractive index of authentic samples A and D, this

agrees with the theory of conventional binary mixtures.

Likewise, for the case of samples B and C from Table 1 (fourth column) there is a

similar trend as for sample A. However, the mixtures have higher values than au-

thentic samples. This is not expected conventionally, usually the mixture refractive

index lies in between the two mixture constituents. If only refractive index is consid-

ered one might confuse adulterated samples with authentic ones. These measure-

ments were performed again after a few months to assess if the values will be differ-

ent, but no substantial changes were noticed. This led to the belief that, the reason for

the abnormal refractive index value of complex winter diesel oil and kerosene mix-

tures may result from chemical interactions, this we demonstrated in (Papers II and

III). There might be even more difficult circumstances of adulteration, for example

25

when adulteration is done by more than one substance. In such a scenario the refrac-

tometer might fail to differentiate the samples, the feasible approach for such a prob-

lem is presented in (Paper IV).

4.4 REFRACTIVE INDEX MEASUREMENTS IN TANZANIA

The field samples in Tanzania were directly measured by Abbe refractometer Atago

H-50. This was necessary to confirm the possibility for detection of adulteration by

onetime measurements. It is also worthy to point out that, the application of this de-

vice to detect adulteration of liquid fuels has never been reported anywhere in liter-

ature. In Table 2 are shown the refractive index measurements for adulterated sam-

ples which were performed at two different temperatures. This was necessary to as-

sess the effect of temperature.

From Table 2, it is obvious that unlike the training set measurements of Table 1

where diesel oils have higher refractive index values than kerosene, here kerosene

has a higher value. Moreover, the variation of refractive index (diesel oil as compared

to kerosene) is large enough. For the case of adulterated samples, the situation is in-

teresting, namely the refractive index value for 5% adulteration is above that of pure

diesel oil. However, for 10% and 15% the value is same. The handheld device is inca-

pable to differentiate between these samples. This is still not a weakness if one is

interested to reveal adulteration because still the value is higher compared to that of

authentic diesel oil, and even higher compared to that of 5%. The adulterated sam-

ples behave normally since the refractive index for both 5% 10% and 15% falls in

between thus, nicely in line with binary mixture rules. For more details including the

volume fill fraction calculations for Tanzanian samples refer (Paper IV).

Table 2: Refractive index data for authentic diesel oil, kerosene and their mixtures

measured in Tanzania.

Sample Adulteration percent Refractive index

n (25˚C)

Refractive index

n (27˚C)

Diesel

0% 1.4440 1.4433

5% 1.4451 1.4444

10% 1.4463 1.4456

15% 1.4463 1.4456

Kerosene

0% 1.4640 1.4644

26

The refractive index values that were measured in Finland (Table 1), were com-

bined with the measured transmittance spectral data for further analysis.

4.5 TRANSMITTANCE MEASUREMENTS

In this section we briefly consider the transmittance measurements for both authentic

and adulterated samples in Vis-NIR spectral range. Moreover, the cuvette reflection

issues are addressed to ensure accurate data for further processing and analysis.

4.5.1 Double optical path length method (authentic samples)

The NIR transmittance spectra of the fuel samples A-D, were measured at room tem-

perature with the aid of a spectrophotometer (Perkin Elmer Lambda 9), and a 5mm

as well as 10mm thick quartz cuvette, the two cuvettes were utilized to try to get rid

of cuvette reflections by double optical path length method. Data pretreatment was

twofold, namely initial baseline correction was done at the beginning by the instru-

ment with empty cuvette, later multiplicative scatter correction (MSC) which takes

care of pathlength errors, baseline shift and interference was done using (PLS-

Toolbox, Eigen vector Research, INC, USA) in Matlab software package. The accu-

racy of spectrophotometer in UV-Vis-NIR range was 0.07% for 1 unit of absorbance,

and the target spectral range for this work was Vis-NIR spectrum. The spectral range

below 2000 nm was chosen, to facilitate the possibility of utilizing the cheap commer-

cial spectrometers operating at this range, for field condition measurements.

Usually during spectroscopic measurements, the reflections form outer and inner

surfaces of the cuvette affects the measurement accuracy of both absorption coeffi-

cient and extinction coefficient of fuel samples. The refractive index value of the

quartz cuvette is not so much different from the fuel samples of this study, and there-

fore reflection losses are rather low. The two- optical path length method was ex-

ploited to get the best estimate of the properties of authentic diesels, it is highly im-

portant to ensure that, the data used is highly immune to external influences, this is

imperative especially for liquids with closer refractive index values. To cancel reflec-

tions by double optical path length method, the Beer Lambert’s law from (Eq. (3.7))

was applied. The path length for 10 mm cuvette is represented by 2d, while the one

for 5 mm cuvette is represented by d. The measured transmittances are denoted here

by T1 = exp(-α(λ)d) and T2 = exp(-α(λ)2d). First, the transmittance ratio r was calculated

by r = T2/T1 = exp(-α(λ)d), and next the estimate of the absorption coefficient was cal-

culated by equation α = (−1/d)ln(r) where d = 5 mm. The calculated absorption coeffi-

cients were further utilized for analysis.

In Fig. 4.2 are shown the Vis-NIR transmittance curves for different fuel samples

at ca. 431 nm to 1600 nm, the double optical pathlength method was utilized to obtain

the transmittance data with less effects from cuvette reflections. It is evident from

(Fig. 4.2) that, in the NIR region at ca. 1200 nm to 1400 nm, the transmission is weak

due to strong absorption of NIR radiation which is due to hydro-carbons of the fuels

27

(C-H stretch). There is overlap in the spectra and there is decrease in transmittance

for sample A as compared to other samples, which is caused by additives which are

present in winter diesel oils. Therefore, the region below 600 nm is useful for differ-

entiating summer from winter diesel oils. Moreover, the transmittance of kerosene is

very close to that of diesel oils, this is one of the reasons why it is difficult to separate

and differentiate adulterated samples. Therefore, a combination of refractive index

and transmission spectrum is considered to address this problem.

Figure 4.2. The transmittance curves for diesel oils samples (A-C), and kerosene sample (D) (Paper II).

Next, the usefulness of double optical pathlength method is demonstrated in the ab-

sorption coefficient curve in Figure 4.3. From (Fig. 4.3) we can see the absorption

coefficient resulting from the ratio of the measurements by two cuvettes (estimated

absorption coefficient for sample A), as well as the measurements performed by 5

mm and 10 mm cuvettes. Based on the curves obtained, the absorption coefficient

resulting from the ratio is above zero line, while the absorption coefficients resulting

from other measurements have values of zero. This is in perfect agreement with the-

ory which suggests that the absorption coefficient for these samples always have a

positive value above zero. Similar case holds for samples B, C, D, and E. The absorp-

tion coefficients resulting from transmittance ratios were utilized in further calcula-

tions.

28

Figure 4. 3. The measured and estimated absorption coefficient curves for sample A 4.5.2 Adulterated samples

Here we briefly present the transmittance curves for samples A and C, the case of

sample B is similar to sample C. In Fig. 4.4 are shown the transmittance curves for

diesel oil (sample A) adulterated by three varying volumes of kerosene (sample D),

at ca. 431 nm to 1600 nm. It is obvious that the transmission is relatively week in the

vicinity of 1200 nm to 1600 nm, this is caused by hydrocarbons of the fuel (C-H

stretch) which strongly absorb light at 1200 nm and 1400 nm. This is the reason why

NIR range is useful for characterizing materials, because materials which have or-

ganic functional groups absorb NIR light. Moreover, there is a clear spectral distinc-

tion between the authentic and adulterated samples, but authentic and kerosene sam-

ple almost overlapp. On the other hand, the curves resulting from 10% and 15% adul-

teration are overlapping in most areas while, the curve resulting from 5% adultera-

tion has slight spectral distinction in certain areas. For the case of 10% and 15% adul-

teration it is difficult to separate them based on spectral features.

The nature of transmission spectrum for sample B and C is pretty much like that

of sample A, but only differs in the Vis region, only the curves for sample C are

shown in Fig. 4.5 the one for sample B deserve similar treatment. Both curves are

overlapping in most areas of Vis range. Furthermore, in NIR region the adulterated

samples are slightly distinguishable, namely the curve for 15% adulteration is

slightly separated from 10% and 15% adulteration. If one exclusively considers the

case of sample C, the situation of screening of adulterated diesel oils is much worse

with respect to the interpretation of the spectral data, moreover, samples C and D

have very similar values. This is one more indicator of the issue of screening fake

diesel oils by their Vis-NIR spectra or the refractive index data. In the next part, we

29

explore other advanced data analysis methods that were used to characterize the

adulterated samples. These were utilized in (Papers II and III)

Figure 4.4. Transmission curves for authentic and adulterated sample A with kerosene at Vis-NIR (Paper III).

Figure 4.5. Transmission curves for authentic and adulterated sample C with kerosene, at Vis-NIR.

30

5 DATA ANALYSIS METHODS

In this part we briefly and theoretically describe several computational methods that

were utilized in this thesis. The methods were applied to calculate several optical

constants both in broader and narrow spectral ranges, the results were further uti-

lized to characterize different samples. These methods include the imaginary refrac-

tive index (extinction coefficient), real refractive index, excess permittivity, imagi-

nary excess permittivity, and finally the method of increase of volume. Except for the

later, the remaining quantities were calculated as a function of wavelength in broader

Vis-NIR spectral range and the results were utilized for sample characterization.

5.1 EXTINCTION COEFFICIENT

The extinction coefficient is the fundamental optical property of a material, which

describe and quantify the decay of the amplitude of incident electric field as light

travels through the material. Therefore, it is a useful parameter for characterization

of various materials. To calculate the imaginary refractive index (k) for both authentic

and adulterated samples, the Vis-NIR transmittance data (chapter 4) were utilized to

obtain the absorption coefficient by using Eq. (3.9). Later these were applied in Eq.

(3.11) to compute the imaginary refractive index. The imaginary refractive index (ex-

tinction coefficient) is the basic optical property which is necessary to enable the cal-

culation of real refractive index, excess permittivity, and imaginary excess permittiv-

ity. Therefore, it is the backbone of our works in (Papers II and III).

5.2 REAL REFRACTIVE INDEX BY SINGLY SUBTRACTIVE KRAMERS-KRONIG RELATION (SSKK)

The real refractive index at one wavelength of the probe light is usually achieved by

onetime measurements using Abbe refractometer. In this thesis, the concept was to

explore the optical properties at a broader spectral range. This provide rich data es-

pecially in NIR absorption band, which is impossible to get if only Abbe refractome-

ter is used. This is only possible if SSKK dispersion relation is applied. Several re-

searchers have explored this method (Peiponen K. -E. & Saarinen J. J, 2009; Ahrenkiel

R, 1971; Peiponen K. -E., 1998) in the past, and have enabled the development of

computational codes which we have utilized in this thesis (Lucarini V., 2005). The

wavelength dependent imaginary refractive index, which was calculated using the

method in (section 5.2) was utilized to obtain the real refractive index by applying

Eqs. (3.12) and (3.13) (Papers II and III).

31

5.3 EXCESS PERMITTIVITY

Usually for problematic samples, the results of transmittance measurements or Abbe

refractometer measurements if considered independently, does not completely solve

the fuel adulteration problem. Therefore, the excess permittivity is explored, by com-

bining the two measurements, to study the difference between the ideal and meas-

ured permittivity. Moreover, through this quantity the presence or absence of chem-

ical interactions in the mixture can be ascertained (Reis J. C., 2009, Ahire S., 1998).

The theory of complex excess permittivity which is dependent on the frequency of

light was recently derived (Iglesias T. P. & Reis J. C. R, 2016) for liquid mixtures.

Moreover, researchers have also proposed some sensor solutions based on this quan-

tity (Perez A. T. & Hadfield M, 2011).

To measure excess permittivity the refractive index values from Table 1 are uti-

lized, these are combined with transmittance measurements in Fig 4.1 and 4.4. Then

together these are applied to calculate the wavelength dependence of refractive index

by using the SSKK relation as explained in (section 5.2). The excess permittivity is

then calculated from the wavelength dependent refractive index in the Vis and NIR

range using Eqs. (3.15) and (3.16). This was utilized in (Papers II and III).

5.4 IMAGINARY EXCESS PERMITTIVITY

In complex liquid measurement cases such as in this study, the excess permittivity

might fail to resolve the small variations that might be useful for sample characteri-

zation. Thus, the imaginary part of complex excess permittivity was investigated.

This was necessary to find the possibility for differentiating in ascending order, the

level of kerosene adulteration in diesel oils. The expression for complex relative ex-

cess permittivity which is related to complex refractive index of a binary liquid mix-

ture was utilized. This method was applied by different researchers in their recent

works, namely to validate the mixing rules for optical constants in the Infrared spec-

tral region (Baranovic G, 2017), for liquid mixture studies in the terahertz (THz) gap

(THz) gap (McGregor J., 2015; Mou S., 2017). To get the expression for imaginary

excess permittivity, all the procedures and fundamentals described in (section 5.3)

must be considered. The imaginary excess permittivity is given by Eq. (3.17) (Paper

III).

5.5 VOLUME INCREASE METHOD (HANDHELD ABBE)

In the previous parts the presented methods are laboratory based and requires fur-

ther modifications in design to suit the challenging field conditions. However, there

do exist in the market a cheaper device (Atago H-50) which is handheld based, the

device is traditionally used for glucose concentration measurements. Initial tests re-

vealed the usefulness of the device especially when the refractive index between the

two liquids present in the mixture, namely diesel oil and kerosene is large enough.

32

Moreover, even when the difference is small there is a novel method that can still be

utilized to detect adulteration and discern the order of adulterant concentration.

Therefore, in this section a novel measurement method that make use of the

handheld device to solve the fuel adulteration problem in field conditions is pre-

sented. The idea of the method is to increase the volume of the authentic sample into

the presumably suspected sample, and then to track the trend of change of refractive

index. Here the ideal and true permittivity idea that was introduced by (Reis J. C.,

2009) is exploited.

Initially the refractive index measurements are performed by the handheld refrac-

tometer both for pure and adulterated diesel oils, these are recorded and thereafter a

small amount of pure diesel oil is added to the suspected sample. If it happens that

the initial reading is still same as the new reading, this is confirmation of authenticity

of the sample. However, upon volume increase if a change is observed in the value

of refractive index, then it is strong indication for possibility of a fake sample. Here

is where the concept of permittivity is applied because it can reveal interactions and

discern abnormalities in permittivity values (excess), then Eq. (3.19) and (3.20) are

utilized and the detailed analysis which is sketched in (Paper IV) is followed.

33

6 RESULTS AND DISCUSSION

This chapter summarize and discuss the most important findings of this thesis.

Firstly, we present the static and dynamic signal measured by the prototype of an

optical sensor (Paper I), the dynamic signal measurement mode is herein demon-

strated for the first time. Secondly, the results of extinction coefficient and real refrac-

tive index as a function of wavelength are also demonstrated, these were further ap-

plied to compute the excess optical properties of fuel samples (Paper II and III).

Thirdly, we present the novel results of excess optical properties, namely excess per-

mittivity and imaginary excess permittivity. These properties reveal new interesting

spectral features (fingerprints), which aid separation and discrimination of adulter-

ated diesel oil samples (Paper II and III). Finally, the novel method that use cheap

handheld Abbe refractometer to detect adulteration and estimate the level of adul-

terant is discussed (Paper IV).

6.1 PROTOTYPE OF AN OPTICAL SENSOR

This section briefly reports on the optical sensor measurements utilizing both the

static and dynamic modes, enabling adulteration detection. The contact angle results

are also presented, which shows similar abnormalities like the measured dynamic

sensor signal.

6.1.1 Detected optical signal

This section of the thesis is somewhat inspired by the work of Bawuah et al (Bawuah

P., 2017), which demonstrated multifaceted application of the commercial handheld

gloss meter initially proposed by Kuivalainen et al (Kuivalainen K., 2010). The sensor

presented here was modified by adding a rough silica glass disk, which holds the

sample and enables the signal measurement. Herein, we present a novel approach

for fuel adulteration detection, and upon slight modification it is possible to use the

proposed sensor for field condition measurements.

In Table 3 (second column), are shown the magnitude of refractive index for sam-

ples A and D together with their mixtures, as measured by accurate table model Abbe

refractometer, which is bulky and nonportable. All the samples can be differentiated

from one another based on their refractive index readings. The readings are in de-

scending order with respect to adulteration percentage, namely the refractive index

keeps decreasing as the percentage of adulteration increases. Two initial measure-

ments were performed prior to liquid introduction on top of glass, in order to study

the behavior of both rough and smooth glass disks prior to any liquid introduction.

Firstly, the measurement was performed on smooth glass where the ambient me-

dium was air alone, the resulting signal reading (S) was 184. Secondly, the disk was

roughened which led to a rough glass also without any liquid on top, the new signal

34

reading (S) was 106. The detailed discussions including the roughening method are

presented in (Paper I).

In Table 3 (third and fourth columns), are also shown the values of the signal (S)

obtained from the prototype after fuel introduction on top of rough glass. Prior to

measurement stability was ensured by fixing the prototype on a stable metallic stand.

The third column shows data measured 1 minute after fuel introduction, while the

fourth column shows the data measured 20 minutes after the liquid was introduced.

From Table 3, kerosene sample D has higher signal reading compared to diesel oil

sample A. Moreover, the signals for adulterated samples show consistency, namely

the signal keeps decreasing as adulteration percentage increases. The interesting be-

havior depicted by the signals in Table 3, enable their separation and distinction with

high accuracy. Unlike the readings of Abbe refractometer whose accuracy is to sev-

eral decimal places, the prototype is accurate to ± 1 S units. The recorded signal was

largely influenced by refractive index mismatch which also leads to scattering. Based

on the results of Table 3, it is possible to differentiate and separate different fuel sam-

ples with high confidence.

Table 3: Refractive index (n) of authentic diesel oil (A) and kerosene (D), measured with a table model of Abbe refractometer, sensor signal, and contact angle. The refractive index of the roughened glass is 1.4570.

Sample

n

S (1 min) S (20 min)

Diesel

1.46373 130 109

Kerosene

1.44230 160 170

Diesel + 5% Kerosene

1.46269 166 133


1.46163 134 126


1.46060 127 114

In a recent review (Bharath L.V,2017), several sensors were described for combat-

ing fuel adulteration (current state of the art sensors). Majority of these sensors deals

solely with Gasoline adulteration which is usually less problematic case as compared

to diesel oils. Moreover, the few sensors that deals with diesel oils (Mishra V., 2016;

Felix V.J., 2015) are invasive, sometimes involves heating, complex fabrication pro-

cess, or involves multivariate analysis, and are laborious. To the contrary our pro-

posed sensor is noninvasive and only a drop or two of the fuel sample is required,

and the measurement time is instant. Moreover, this sensor is portable and easy to

use.

Next, if the results of refractive index measurements in (Table 3) with those in

(Table 1) are compared, the refractive index is changing for the samples. The change

is caused by temperature variations, as well as possible chemical interactions be-

tween the samples. The change for the authentic samples is in the fourth decimal, this

35

is alarming because, if the cause of variation was only temperature based then the

change in refractive index for adulterated samples also could be expected in the

fourth decimal. However, this is not the case because the change in refractive index

for adulterated samples is in the third decimal, which lead us to believe the possibil-

ity of chemical interaction for the mixtures. This stimulated us to deal with excess

permittivity study to see if it can lead to a more plausible solution for fake diesel

studies (Paper II and Paper III). For more detailed explanation of the results of the

prototype refer (Paper I).

6.1.2 Contact angle measurements

The measurement of the contact angle for both samples of this study was done on a

glass plate which is smooth. For kerosene the value was 11. 2, for 5% was 22.2, for

10% was 24.7, for 15% was 21.1, and for nonadulterated diesel oil it was 25.9. In-

terestingly the adulterated samples values are between those for authentic diesel oil

sample A and kerosene sample D. However, it is obvious that there is no a clear order

on the contact angle, namely 10% has a higher value compared to 5% and 15%. This

might also suggest presence of unusual chemical activity in the mixtures, supporting

the initial suspicion (section 6.1.1). The contact angle is one way of separating and

differentiating the samples. However, the devices are rather expensive and not port-

able for field measurements. Therefore, in this work it was applied to ascertain the

differences between samples and possibility of chemical interactions.

6.1.3 Dynamic signal from fuel drop spreading over a rough glass

Another novel method that enables fuel adulteration detection is the measurement

of time dependent backscattered signal (TDBS), which has not been published. The

prototype of a handheld sensor is incorporated with a wireless signal detection unit,

namely wireless connection to the personal computer (PC) which enables monitoring

of the time dependent signal through a PC. In Fig. 6.1 are shown the measured TDBS

from the prototype sensor with kerosene on top of the rough surface. It is obvious

from (Fig. 6.1) that, for kerosene there is a gradual rise with more apparent hysteresis,

to a maximum and subsequent decrease in the TDBS signal for the rough surface.

On the other hand, Fig. 6.2 shows the TDBS from the prototype sensor, with au-

thentic and adulterated diesel oils on top of the rough surface. It is obvious from (Fig.

6.2) that, pure diesel oil on the rough surface show slow but continuous increase in

TDBS. However, adulterated diesel oils exhibit interesting behavior in terms of the

nature of the TDBS signal and positioning by initially showing a raise and subse-

quently decrease in signal strength as compared to that of the pure diesel oil.

The difference in the behavior of fuels on the rough glass surface enable screening

of fake diesel oils. This demonstrates the second novel approach for fuel adulteration

detection by the same prototype. Moreover, the behavior of 10% adulterated sample

is similar both in contact angle as well as in the detected signal (Fig. 6.2). In the con-

tact angle measurement refer (section 6.2), the value for authentic diesel oil is closer

36

to that of 15% adulteration, same case holds for the case of time dependent signal.

Moreover, in the time dependent signal the 10% adulteration sample has a plateau

type behavior across most part of the measured time duration, showing a strange

behavior which cannot be detected by refractive index readings. This shows the pe-

culiarity of this measurement method as compared to the normal measurement mode

of the device.

Figure 6.1. Time dependent backscattering signal (S) from the prototype sensor with kerosene on top of the rough surface.

Figure 6.2. Time dependent backscattering signal (S) from the prototype sensor with authentic and adulterated diesel oils on top of the rough surface.

37

6.1.4 Summary

The prototype of an optical sensor was realised, this prototype detects a combination

of both scattered as well as reflected light from the interface between the glass and

fuel film. Firstly, the measurement mode which provide one averaged signal value

was utilised, with this mode it was possible to differentiate varying mixtures of adul-

terated diesel oils with high confidence. Secondly, the other measurement mode of

the prototype was also investigated. With the second mode it was possible to meas-

ure the time dependent backscattered signal for different diesel oil mixtures, signals

from various samples assume different shapes, providing another novel alternative

for adulteration detection.

On the other hand, unlike the abbe refractometer readings which can only be dis-

tinguished in second and third decimal places, the readings from the handheld de-

vice can easily be interpreted by local inspectors because of differences in second

digit for different adulterated fuel samples. The drawback of the handheld device is

lack of mechanical stability especially in field conditions, this should be taken care of

for the device to perform accurately in field conditions. Moreover, even though the

initial prototype sensor is expensive, it is possible to design low cost devices, thanks

to the availability of cheap CCD sensors and lasers.

6.2 EXTINCTION COEFFICIENT

Extinction coefficient was used by various researchers as analytical technique for

characterizing materials especially consumer oils (Vanak Z. P., 2010; Amereih S.,

2014). In this study, we were partly motivated by the pioneer work of Dombrovsky

et al (Dombrovsky L. A., 2003) which studied the spectral properties of diesel fuel

droplets across a broader spectral range. The positive results that were obtained in

this section led us to investigate other optical properties, such as wavelength depend-

ent refractive index, excess permittivity, and imaginary excess permittivity.

Here we study the wavelength dependent refractive index for one summer and

one winter diesel oils, namely samples A, B and D (Paper III), similar procedures

were followed for sample C (Paper II). The wavelength dependent extinction coeffi-

cient was calculated by using Eq. (3.11) and the transmittance data in Fig. 4.4. To

demonstrate these results, we utilized the data for sample A, similar trend is exhib-

ited for sample B. From Fig. 6.3 we can make interesting observations regarding the

different signals from 1190 nm to 1220 nm. Firstly, there do exist fingerprints with

higher peaks at 1195 nm and lower peaks at 1215 nm. Secondly, the behaviour of

kerosene is contrary to that of diesel oil, therefore it is possible to use the magnitude

of the height ratios to differentiate pure diesel oils from kerosene.

The height property is insignificant for separating the different adulterated sam-

ples mainly because, the height on the left side at 1195 nm is constantly higher when

compared to that at 1215 nm. Therefore, the attempt to apply the ratio of heights for

identification purposes fail in this case. Moreover, the curves are not ordered in the

38

order of increase or decrease of adulteration percentage, therefore lacking potential

to separate and discriminate the different samples.

Figure 6.3. The zoomed extinction coefficient curves for kerosene, authentic and adulterated sample A in the vicinity of 1190 nm to 1220 nm (Paper III).

Next, we explore another interesting alternative in Fig. 6.4 where the curves for

same samples as in (Fig. 6.3) are depicted, but now in the spectral range from 1390

nm to 1427 nm. The nature of the curves is contrary to those in (Fig. 6.3), namely there

is no polarity for both the left and the right peaks. The curves are arranged in accord-

ance to adulteration percentages, namely the lower curve is for the lowest adultera-

tion percentage (5%) while the higher curve is for the highest adulteration percentage

(10%). Thus, the relationship between the extinction coefficient curves and adultera-

tion percentage is of a linear fashion, which enables separation and discrimination.

It is possible to resolve and separate well the curves for mixtures of sample A and

sample D, while the curves for mixtures of sample B and sample D almost overlap.

For the curves of sample B (refer Paper III). Both curves for adulterated samples are

higher compared to authentic diesel oil and kerosene, from the curves in (Fig. 6.3(b))

already one can observe the excess extinction coefficient, which implies excess refrac-

tive index and consequently excess permittivity. This makes the spectral region of

1390 nm to 1420 nm very important for separating the samples with different adul-

teration percentages. Because of these results we were able to explore other optical

properties, starting first with wavelength dependent refractive index, and then the

concept of imaginary excess permittivity. These were necessary to assess the differ-

ences between samples adulterated by different volume fractions of kerosene, in the

39

spectral region around 1400 nm, therefore making use of the recent theory proposed

in (Iglesias T. P. & Reis J. C. R, 2016).

Figure 6.4. The zoomed extinction coefficient curves for kerosene, authentic and adulterated sample A in the vicinity of 1390 nm to 1427 nm (Paper III).

6.3 WAVELENGTH DEPENDENT REFRACTIVE INDEX

Next, as it was for the case of (section 6.2) the refractive index as a function of wave-

length was obtained by using Eqs. (3.12) and (3.13) which were estimated by SSKK

integration in the finite spectral range from 431 nm to 1600 nm. In Figure 6.5(a) are

shown the wavelength dependent refractive index curves for sample A. Strong vari-

ations are observed in the refractive index values especially for the regions exhibiting

stronger absorptions, namely around 1200 nm and 1400 nm. The curve for 15% is

highly distinct while those for 10% and 15% are almost overlapping. The strange be-

havior of sample A and especially that of 10% is an indicator of possible chemical

interactions, these were suspected also in (section 6.1). The behavior of sample B in

Fig. 6.5(b) is slightly different in the sense that both curves are clearly distinct from

one another. The wavelength dependent refractive index data were later utilized to

calculate the imaginary excess permittivity by applying Eq. (3.17). For more detailed

description of the Imaginary excess permittivity calculation refer (Paper III).

Next in Fig. 6.5(c) are shown the wavelength dependent refractive index curves.

The curve for authentic sample C is the lowest while, the curves for 5% adulteration

is the highest, followed by 10% and 15% consecutively. Moreover, we observe in (Fig.

6.5(c)) that, for all three samples namely, 5%, 10% and 15% adulteration, the curves

are clearly distinct from each other. Therefore, SSKK provides better alternative for

separating samples adulterated by different volumes of kerosene (Paper II).

40

In this section the wavelength dependent refractive index was explored to study

adulterated diesel oil samples. Based on the results (Figs. 6.5(a), (b) and (c)), the sam-

ples are distinct. However, there is no a specific order followed by adulterated sam-

ples. For example (in the case of sample B the curve for authentic diesel oil appears

at the middle followed by 10% above it and 5% even further above while the curve

for 15% is the lowest). Therefore, in the next section we explore the excess optical

properties which reveal the presence of kerosene in diesel oils. Moreover, the prop-

erties enable separation and discrimination of adulterated samples, in accordance to

the volume of kerosene (Paper II and III).

41

a

b

c Figure 6.5. The calculated refractive index curves for authentic and adulterated samples (a) Sample A (b) Sample B (Paper III) and (c) Sample C (Paper II).

42

6.4 EXCESS OPTICAL PROPERTIES

In this section we briefly report on the results of two most important findings of this

thesis, namely excess permittivity and imaginary excess permittivity.

6.4.1 Excess permittivity

The refractive index values for samples A, B and C (both authentic and adulterated)

are presented in Table 1 (chapter 4). These values were utilized for calculating the

excess permittivity using Eqs. (3.15) and (3.16). The results for excess permittivity are

presented in Table 4.

Table 4: Excess permittivity of adulterated fuel samples A-C.

Sample

Volume fraction

Of Kerosene

Excess permittivity (10-3)

A 5%

-0.30

10%

2.63

15%

2.06

B 5%

4.25

10%

4.13

15%

2.83

C 5%

3.14

10%

3.17

15%

3.20

From (Table 4) the magnitude of excess permittivity (at 589 nm) for all the samples

is small, this implies the existence of less chemical interactions. There is negative sign

in the value of excess permittivity for sample A which is very different from all the

other samples. Moreover, there is a unique behaviour regarding the value for sample

A, namely 10% sample has highest value of excess permittivity in comparison to the

values for 5% and 15%. (Paper III) This seemingly strange behaviour agrees with the

behaviour of the time dependent back scattered signal measured by the new meas-

urement mode of the prototype in (Paper I). The dynamic signal behaviour depicted

in (section 6.1) shows a rather constant signal for 10% contrary to the plateau type

signal of 5% and 15%. For the case of samples B and C only positive values for excess

permittivity are observed. The values for sample B are interesting, namely 5% sample

has the highest value while 15% has the lowest value, moreover, the values for 5%

43

and 10% are almost same whereas the value for 15% is very low. On the other hand,

sample C has constant excess permittivity value, this might partly be due to the close-

ness of its refractive index value with that of kerosene. Therefore, it is difficult to

differentiate adulterated samples originating from sample C based on the single

point (589 nm) excess permittivity value.

The wavelength dependent excess permittivity is important for exploring the op-

tical behavior of different authentic and adulterated samples across a broader Vis-

NIR spectral range. For this purpose, sample C is considered because according to

Table 4 it is the most difficult case which gives almost constant value for different

adulteration percentages. The wavelength dependent extinction coefficient was cal-

culated using Eqs. (3.9) and (3.11). The results were utilized for calculating the wave-

length dependent refractive index using Eqs. (3.12) and (3.13), transmittance values

from Figs. 4.1 and 4.4, and refractive index values from Table 1. The results of Fig

6.5(c) were utilized for calculating the wavelength dependent excess permittivity by

making use of Eqs. (3.15) and (3.16), and the results were applied to determine per-

mittivity as a function of wavelength using Eq. (3.14). Fig. 6.6 shows the results of

excess permittivity for two samples, namely 5% and 15%, there are clear nice over-

lapping fingerprints at ca. 1200 nm and 1400 nm which are indicators of adulteration

in the measured samples. Therefore, it is possible to discern the presence of kerosene

in diesel oils based on excess permittivity curves. This is a novel finding thanks to

this thesis.

Figure 6.6. Excess permittivity for mixtures of samples C and kerosene (D) (Paper II). 6.4.2 Imaginary Excess permittivity

The other interesting part of this thesis is the imaginary excess permittivity as a func-

tion of wavelength, for different adulterated samples. These were calculated using

44

Eq. (3.17), and by utilizing the wavelength dependent refractive index data depicted

in Fig 6.5 (a) and (b). In Fig. 6.7(a) the curves for imaginary excess permittivity of

adulterated versions of sample A are shown. The order of the curves corresponds to

the percentage of adulteration, namely the excess permittivity curve for 5% is the

lowest while that for 15% is the highest. It is possible to clearly and with confidence

separate and discriminate the samples based on these curves.

The results for sample B are presented in Fig. 6.7(b) where similar trend regarding

the location and separation is observed. However, unlike the case of sample A where

the curves for 5% and 10% are somewhat close to each other (Fig. 6.7(a)), here the

curves for 5% and 10% are more clearly distinct from one another. The interesting

results around 1400 nm are promising and open doors for the development of meas-

urement methods, which operate in the small spectral range. This will greatly serve

analysis time and will enable timely measurements to detect adulteration.

The current state of the art technologies applied for fuel adulteration studies, is

based on spectroscopic measurements coupled with multivariate analysis (Bassbasi

M., 2013; Marcio J. C. P., 2011; Fazal M., 2017). These methods rely on a broader spec-

tral range and require commercial multivariate analytical software packages such as

Unscrambler X, and PLS toolbox for Matlab, for data analysis. These are usually ex-

pensive and requires long time (spectroscopic measurements and analysis), and spe-

cial training, therefore usually suited for laboratory applications. One of the quests

of this thesis is to come up with methods that will enable the development of field

condition measurement devices. Therefore, both novel methods, namely excess per-

mittivity and imaginary excess permittivity are potential candidates for such a pur-

pose. We infer that, these results can lead to more promising twofold solutions.

Firstly, the spectral region around 1200 nm and 1400 nm have been identified as the

most important regions for identifying the presence of kerosene in diesel oils. There-

fore, it is possible to utilize this narrow band and reduce the processing time for com-

mercial software packages. Secondly, thanks to the novel results at 1400 nm, more

doors are open for devices that exploit the concept of imaginary excess permittivity

at a very narrow wavelength around 1400 nm.

45

a

b

Figure 6.7. The zoomed imaginary excess permittivity for samples adulterated by kerosene in the vicinity of 1390 nm to 1427 nm (a) Sample A (b) Sample B (Paper III).

6.4.3 Summary

The real and imaginary excess permittivity of diesel oils adulterated by kerosene

were studied. This was achieved by combining data measured by spectrophotometer

and Abbe refractometer, and thereafter utilizing the SSKK analysis to calculate wave-

46

length dependent refractive index, excess permittivity and imaginary excess permit-

tivity. The excess permittivity reveals nice spectral fingerprints at ca. 1200 nm and

1400 nm, which indicates the presence of kerosene in diesel oils. The fingerprints are

unavailable if refractive index or transmittance data are considered independently.

The imaginary excess permittivity method provides nicely ordered curves around

1400 nm, which clearly separate diesel oils adulterated by different volume fractions

of kerosene.

This is the first time in applied optics that the model-independent SSKK analysis

is utilized and proposed for excess permittivity and imaginary excess permittivity

studies of any binary or multi-mixture liquids. The proposed methods in the present

form are laboratory based. Nevertheless, due to availability of portable NIR spectro-

photometers and small digital handheld refractometers, it is possible to design a

measurement unit which is equipped with a laptop for field measurements by fuel

regulatory authorities. The disadvantage of the system is requirement of well trained

personnel, with background in data analysis packages such as Matlab, to carry out

the measurements and analysis.

6.5 HANDHELD REFRACTOMETER METHOD

It is possible to use some of the already existing devices to solve current measurement

problems. Herein, this is demonstrated, namely a novel measurement method incor-

porated with the handheld Abbe refractometer was applied to solve fuel adultera-

tion. We briefly consider a training set in laboratory conditions, and later the novel

method is confirmed by field measurements.

6.5.1 Training set

Here we present the results of a training set in the laboratory depicting the directly

observed relationship between the ideal permittivity curve, and the adulterated

sample as the volume of authentic diesel oil is increased. In Fig. 6.8 are shown the

curves for sample A where the dashed line represents the permittivity of ideal

mixture. This was calculated by the modified ideal law Eq. (3.20) and utilizing data

of Table 1. The differences between the measured permittivity and ideal permittivity

is directly observed from the graph, because the points and the curve are at separate

locations. However, as the volume of the adulterant is increased, the measured

permittivity approaches the ideal permittivity curve. The case of sample B is even

much complex because the refractive index between diesel oil (sample B) and

kerosene (sample D) are very close, unlike that of samples A and D. As was

mentioned earlier, the proposed method works much better when the refractive

index difference is large enough.

47

Figure 6.8. Ideal permittivity for mixtures of sample A, and the measured permittivity

values for adulterated samples obtained by table model Abbe refractometer (Paper IV).

6.5.2 Field measurements

Next, we present the application of a novel measurement method for field adultera-

tion screening in Tanzania, these are depicted in Fig. 6.9. These were calculated using

Eq. (3.20) and data from Table 2. The nature of the curves is different from those of

laboratory measurements in (Fig. 6.8), this is caused by interchange in roles where

by kerosene has higher permittivity value as compared to authentic diesel oil. The

true permittivity points are at separate locations thus deviates from the ideal permit-

tivity curves especially those for 10% and 15%. This already is an indicator for excess

permittivity. From Fig. 6.9 it holds similarly to (Fig. 6.8) that, as the volume of au-

thentic diesel oil is increased into the suspected sample, the values for true permit-

tivity keeps approaching those of ideal permittivity. Moreover, the trend is similar

for different curves at different temperatures. Therefore, this measurement method

is feasible and can be exploited to scan for adulteration (Paper IV).

48

Figure 6.9. Ideal permittivity for Tanzanian sample, and the true permittivity values for

adulterated samples obtained by handheld Abbe refractometer data (Paper IV).

6.5.3 Summary

In this work we have demonstrated for the first time the application of handheld

Abbe refractometer which traditionally measures sugar concentration, for possible

application to detect fuel adulteration. To test the suspected sample firstly, pure die-

sel oil is added and there after the concepts of ideal and true permittivity are ex-

ploited, to study the existing relationship between the true permittivity of the meas-

ured sample and the ideal permittivity. Obviously, the Tanzanian sample performs

better because of considerable value differences between diesel oil and kerosene.

The advantages of this system are low cost of handheld Abbe refractometer, sim-

plicity of operation of the device which does not require any sophisticated chemo-

metrics or data analysis methods, and portability as well as stability of the handheld

device under field conditions. The local inspectors only require simple training on

how to compare the data with calibration curves. With this sensor it is possible to

solve many common fuel adulteration problems especially in poor countries, without

the need to take the samples to the laboratory.

49

7 CONCLUSION AND OUTLOOK

As a remedy to environmental pollution it is imperative to combat fuel adulteration.

Therefore, in this thesis we have successfully demonstrated several solutions. Firstly,

we have developed an optical sensor by modifying a commercial handheld gloss me-

ter to include a removable sensor head with roughened glass, which is able to screen

the adulterated diesel oils based on the mismatch of refractive index at the rough

glass-fuel interface. Secondly, two data analysis methods were realized which com-

bines refractive index measurements together with transmission data inversion using

SSKK relations. These were utilized to obtain the wavelength dependent relative ex-

cess permittivity, and wavelength dependent imaginary excess permittivity which

reveals hidden spectral fingerprints for adulterated diesel oils and discriminate dif-

ferent adulteration levels. Finally, a method based on application of cheap commer-

cial handheld refractometer was demonstrated as an alternative for fuel adulteration

detection in poor countries.

For the first time this thesis reports on excess permittivity study of diesel oils adul-

terated by kerosene. Moreover, this is the first time in applied optics that rather gen-

eral K-K analysis which is based on the principle of causality is applied to assess both

real excess permittivity and imaginary excess permittivity of binary liquid mixtures

especially for adulterated fuels.

In comparison, the current state of the art technologies for fuel adulteration de-

tection depends on a broader spectral band data in the Vis-NIR, coupled with multi-

variate analysis (Bassbasi M., 2013; Marcio J. C. P., 2011; Fazal M., 2017). These meth-

ods are very accurate and can quantify very small volume of adulterants. However,

broader spectral range consumes more time in processing, and the method is not fea-

sible for field measurements. Therefore, this thesis partly addresses those limitations

in two ways. Firstly, our proposed and demonstrated sensors upon further modifi-

cation can be applied for field measurements. Moreover, the proposed essential re-

gion around 1400 nm and 1200 nm can be exploited in multivariate analysis, and for

designing sensors which utilize the excess permittivity and imaginary excess permit-

tivity concepts, rather than the whole Vis-NIR band.

Recently, researchers have demonstrated the possibility of developing state of the

art fuel sensing solutions based on metamaterials (Rawat V., 2016), Optical wave-

guides (Dutta A., 2013), and micro as well as nano fabrication technologies (Mishra

V., 2016). Moreover, in a recent dissertation the author has demonstrated the possi-

bility of using enhanced Raman scattering to enable nano structures to be applied as

sensors. These sensors can detect molecules in extremely low concentrations. Thanks

to these interesting breakthroughs, fuel and other liquid adulteration studies will be

taken to another level in near future.

50

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ISBN 978-952-61-2893-1ISSN 1798-5668


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DEVELOPMENT OF OPTICAL MEASUREMENT TECHNIQUES AND DATA ANALYSIS METHODS FOR SCREENING

OF ADULTERATED DIESEL OILS


Fuel adulteration is one of the contributors to climate change. As a partial solution to

fuel adulteration, this thesis proposes novel approaches by using handheld refractometer

and novel handheld sensor to combat fuel adulteration especially in field conditions.

Moreover, for the first time in applied optics the concepts of excess permittivity and

imaginary excess permittivity are applied to resolve complex fuel adulteration problems.

These novel approaches save as new openings demonstrating the potential of excess

permittivity analysis in fraud fuel detection.


30887153_UEF_Vaitoskirja_NO_316_Elphace_Boniphace_LUMET_cover_18_09_12.indd 1 12.9.2018 8.51.23

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